# Green-book Estimators

## Green-Book (GB) module overview

`FIESTA`‘s Green-Book (GB) module calculates population estimates and their sampling errors based on Bechtold and Patterson’s (2005), ’Green-Book’ for FIA’s nationally-consistent, systematic annual sample design, chapter 4 (Scott et al. 2005). FIA’s sample design is based on 2 phases: the first phase uses remotely-sensed data to stratify the land area to increase precision of estimates; while the 2nd phase obtains photo and ground observations and measurements for a suite of information across a hexagonal grid, each approximately 6000 acres in size. The associated estimators and variance estimators are used for area and tree attribute totals with the assumption of a simple random, stratified design and double sampling for stratification. Adjustment factors are calculated by estimation unit and strata to account for nonsampled (nonresponse) conditions.

Functions include non-ratio estimators for area and tree estimates by domain and ratio-of-means estimators for per-acre and per-tree estimates within domain. In addition, `FIESTA` adjusts for nonsampled conditions, supports post-stratification for reducing variance, and reports by estimation unit or a summed combination of estimation units. Output from the Green-Book module was tested and compared to output from FIA’s publicly-available online tool (EVALIDator) for state-level population estimates and associated sampling errors generated from the FIA Database (FIADB).

## Objective of tutorial

The Green-Book estimators can be used with FIA’s standard state-level population data (i.e, Evaluation) from the FIA database (FIADB) and also population data from a custom boundary. The population data includes a set of FIA plot data and summarized auxiliary information for post-stratification, including a table of area by estimation unit within the population, and a table of strata proportions by estimation unit. This tutorial steps through several examples using FIESTA’s Green Book module, for four different populations: (POP1) an FIA standard Evaluation, Wyoming 561301; (POP2) a custom boundary with one population, Bighorn National Forest; (POP3) a custom boundary with sub-populations, Bighorn National Forest Districts; and (POP4) an FIA standard Evaluation, Rhode Island 441901, stored in a SQLite database. All examples can be used with any population, standard or custom.

## GB Examples

### GB Example Data

View GB Example Data

Example FIA plot data from FIADB

The examples use FIA plot data from FIA Evaluation 561301, including three inventory years of field measurements in the state of Wyoming, from FIADB_1.7.2.00, last updated June 20, 2018, downloaded on June 25, 2018, and stored as internal data objects in `FIESTA`.

Wyoming (WY), Inventory Years 2011-2013 (Evaluation 561301)

Data Frame | Description
--- | ---
WYplt | WY plot-level data
WYcond | WY condition-level data
WYtree | WY tree-level data

Example Auxiliary data

Auxiliary data for state-level estimates, including plot-level estimation unit and stratum assignments; area by estimation unit; and pixel counts by strata class and estimation unit, were downloaded from FIADB at the same time, from the same FIA Evaluation (i.e., 561301), and stored as internal data objects in `FIESTA`. Estimates using auxiliary data from FIADB can be compared with EVALIDator estimates, using the 2013 evaluation (https://apps.fs.usda.gov/fiadb-api/evalidator).

Auxiliary data for the custom boundaries are summarized from spatial layers stored as external objects in FIESTA, originating from the USDA Forest Service, Automated Lands Program (ALP; 2018) and from a 250m resolution, Moderate Resolution Imaging Spectroradiometer (MODIS), classified map, reclassified from 3 to 2 classes: 1:forest; 2:nonforest (Ruefenacht et al. 2008)

Wyoming (WY), Auxiliary data from FIADB (Evaluation 561301)

Data Frame | Description
--- | ---
WYpltassgn | WY plot-level data with strata and estimation unit assignments
WYunitarea | WY estimation unit look-up table with total acres by estimation unit (ESTUNIT)
WYstratalut | WY strata look-up table with pixel counts (P1POINTCNT) by strata and estimation unit

Wyoming (WY), Auxiliary data from other sources

External data | Description
--- | ---
WYbighorn_adminbnd.shp | Polygon shapefile of WY Bighorn National Forest Administrative boundary1
WYbighorn_districtbnd.shp | Polygon shapefile of WY Bighorn National Forest District boundaries2
WYbighorn_forest_nonforest_250m.tif | GeoTIFF raster of predicted forest/nonforest (1/0)3

1USDA Forest Service, Automated Lands Program (ALP). 2018. S_USA.AdministrativeForest (http://data.fs.usda.gov/geodata/edw). Description: An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area.

2USDA Forest Service, Automated Lands Program (ALP). 2018. S_USA.RangerDistrict (http://data.fs.usda.gov/geodata/edw). Description: A depiction of the boundary that encompareasses a Ranger District.

3Based on 250m resolution, Moderate Resolution Imaging Spectroradiometer (MODIS), classified map, reclassified from 3 to 2 classes: 1:forest; 2:nonforest. Projected in Albers Conical Equal Area, Datum NAD27 (Ruefenacht et al. 2008).

### Set up

First, you’ll need to load the `FIESTA` library:

    library(FIESTA)

Next, you’ll need to set up an “outfolder”. This is just a file path to a folder where you’d like `FIESTA` to send your data output. For this vignette, we have set our outfolder file path as a temporary directory.

    outfolder <- tempdir()

### Get auxiliary data for custom examples

Now, we need to get the auxiliary data for the custom boundaries. The FIESTA spGetStrata function is a spatial wrapper function to facilitate extraction and summary of user-defined spatial data used for post-stratification. The function uses the FIESTA spExtractPoly and spExtractRast functions to subset (i.e., clip) plots to the boundary and extract values from estimation unit (i.e., polygon) and strata values (i.e., raster) to plot center locations, respectively. Other internal spatial functions calculate stratum pixel counts and area by estimation unit. If a polygon strata layer is given, the FIESTA spPoly2Rast function converts the polygon layer to raster before calculating strata weights.

Our custom examples demonstrate how to get data for one area of interest, or population (e.g, Bighorn National Forest) and for one area of interest, with multiple estimation units, or subpopulations (e.g., Bighorn National Forest Districts).

Bighorn National Forest

View Getting Strata Data

    # File names for spatial layers, stored as external data objects in FIESTA.
    WYbhfn <- system.file("extdata", "sp_data/WYbighorn_adminbnd.shp", package = "FIESTA")
    fornffn <- system.file("extdata", "sp_data/WYbighorn_forest_nonforest_250m.tif", package = "FIESTA")

    # Get estimation unit and strata information for Bighorn National Forest.
    stratdat.bh <- spGetStrata(xyplt = WYplt,
                               uniqueid = "CN",
                               unit_layer = WYbhfn,
                               strat_layer = fornffn,
                               spMakeSpatial_opts = list(xvar = "LON_PUBLIC",
                                                         yvar = "LAT_PUBLIC",
                                                         xy.crs = 4269))

    # Get names of output components
    names(stratdat.bh)

output

    ##  [1] "bnd"        "pltassgn"   "pltassgnid" "unitarea"   "unitvar"
    ##  [6] "unitvar2"   "areavar"    "areaunits"  "stratalut"  "strvar"
    ## [11] "getwt"      "strwtvar"

    # Plot assignment of strata and estimation unit (ONEUNIT, STRATUMCD)
    head(stratdat.bh$pltassgn)

output

    ##               CN INVYR STATECD CYCLE UNITCD COUNTYCD  PLOT MEASYEAR RDDISTCD
    ## 1 40404876010690  2012      56     3      2        3 83143     2012        6
    ## 2 40404879010690  2011      56     3      2        3 80153     2011        6
    ## 3 40404921010690  2013      56     3      2        3 85403     2013       NA
    ## 4 40404930010690  2012      56     3      2        3 89093     2012        8
    ## 5 40404939010690  2012      56     3      2        3 86981     2012        6
    ## 6 40404940010690  2013      56     3      2        3 85570     2013        8
    ##   NF_SAMPLING_STATUS_CD PLOT_STATUS_CD NF_PLOT_STATUS_CD NBRCND NBRCNDSAMP
    ## 1                     0              1                NA      2          2
    ## 2                     0              1                NA      1          1
    ## 3                     0              2                NA      1          1
    ## 4                     0              1                NA      1          1
    ## 5                     0              1                NA      2          2
    ## 6                     0              1                NA      1          1
    ##   NBRCNDFOR CCLIVEPLT        FORNONSAMP        PLOT_ID ONEUNIT STRATUMCD
    ## 1         2      67.5    Sampled-Forest ID560200383143       1         1
    ## 2         1      66.0    Sampled-Forest ID560200380153       1         1
    ## 3         0       0.0 Sampled-Nonforest ID560200385403       1         1
    ## 4         1      45.0    Sampled-Forest ID560200389093       1         1
    ## 5         1      54.0    Sampled-Forest ID560200386981       1         1
    ## 6         1      52.0    Sampled-Forest ID560200385570       1         1

    # Area by estimation unit
    stratdat.bh$unitarea

output

    ##   ONEUNIT ACRES_GIS
    ## 1       1   1112401

    #  Pixel counts and strata weights (strwt) by strata and estimation unit
    stratdat.bh$stratalut

output

    ##   ONEUNIT STRATUMCD P2POINTCNT     strwt P1POINTCNT P1POINTCNTFOR
    ## 1       1         1      52289 0.7260344         41            33
    ## 2       1         2      19731 0.2739656         15             4

    # Variable names
    stratdat.bh$unitvar        # Estimation unit variable

output

    ## [1] "ONEUNIT"

    stratdat.bh$strvar         # Strata variable

output

    ## [1] "STRATUMCD"

    stratdat.bh$areavar        # Area variable

output

    ## [1] "ACRES_GIS"

Bighorn National Forest Districts

View Getting Strata Data (Districts)

    # File names for external spatial data
    WYbhdistfn <- system.file("extdata", "sp_data/WYbighorn_districtbnd.shp", package = "FIESTA")
    fornffn <- system.file("extdata", "sp_data/WYbighorn_forest_nonforest_250m.tif", package = "FIESTA")

    # Get estimation unit and strata information for Bighorn National Forest Districts
    stratdat.bhdist <- spGetStrata(xyplt = WYplt,
                                   uniqueid = "CN",
                                   unit_layer = WYbhdistfn,
                                   unitvar = "DISTRICTNA",
                                   strat_layer = fornffn,
                                   spMakeSpatial_opts = list(xvar = "LON_PUBLIC",
                                                             yvar = "LAT_PUBLIC",
                                                             xy.crs = 4269))

    # Get names of output list components
    names(stratdat.bhdist)

output

    ##  [1] "bnd"        "pltassgn"   "pltassgnid" "unitarea"   "unitvar"
    ##  [6] "unitvar2"   "areavar"    "areaunits"  "stratalut"  "strvar"
    ## [11] "getwt"      "strwtvar"

    # Plot assignment of strata and estimation unit (DISTRICTNA, STRATUMCD)
    head(stratdat.bhdist$pltassgn)

output

    ##               CN INVYR STATECD CYCLE UNITCD COUNTYCD  PLOT MEASYEAR RDDISTCD
    ## 1 40404876010690  2012      56     3      2        3 83143     2012        6
    ## 2 40404879010690  2011      56     3      2        3 80153     2011        6
    ## 3 40404921010690  2013      56     3      2        3 85403     2013       NA
    ## 4 40404930010690  2012      56     3      2        3 89093     2012        8
    ## 5 40404939010690  2012      56     3      2        3 86981     2012        6
    ## 6 40406947010690  2011      56     3      2       33 88166     2011        6
    ##   NF_SAMPLING_STATUS_CD PLOT_STATUS_CD NF_PLOT_STATUS_CD NBRCND NBRCNDSAMP
    ## 1                     0              1                NA      2          2
    ## 2                     0              1                NA      1          1
    ## 3                     0              2                NA      1          1
    ## 4                     0              1                NA      1          1
    ## 5                     0              1                NA      2          2
    ## 6                     0              1                NA      3          2
    ##   NBRCNDFOR CCLIVEPLT        FORNONSAMP        PLOT_ID
    ## 1         2      67.5    Sampled-Forest ID560200383143
    ## 2         1      66.0    Sampled-Forest ID560200380153
    ## 3         0       0.0 Sampled-Nonforest ID560200385403
    ## 4         1      45.0    Sampled-Forest ID560200389093
    ## 5         1      54.0    Sampled-Forest ID560200386981
    ## 6         1      14.0    Sampled-Forest ID560203388166
    ##                       DISTRICTNA STRATUMCD
    ## 1 Medicine Wheel Ranger District         1
    ## 2 Medicine Wheel Ranger District         1
    ## 3 Medicine Wheel Ranger District         1
    ## 4 Medicine Wheel Ranger District         1
    ## 5 Medicine Wheel Ranger District         1
    ## 6 Medicine Wheel Ranger District         1

    # Area by estimation units (Districts)
    stratdat.bhdist$unitarea

output

    ##                       DISTRICTNA ACRES_GIS
    ## 1 Medicine Wheel Ranger District  364522.8
    ## 2   Powder River Ranger District  334333.7
    ## 3         Tongue Ranger District  413774.9

    # Pixel counts and strata weights (strwt) by strata and estimation unit
    stratdat.bhdist$stratalut

output

    ##                       DISTRICTNA STRATUMCD P2POINTCNT     strwt P1POINTCNT
    ## 1 Medicine Wheel Ranger District         1      14472 0.6127789          9
    ## 2 Medicine Wheel Ranger District         2       9145 0.3872211          7
    ## 3   Powder River Ranger District         1      15251 0.7044667         13
    ## 4         Tongue Ranger District         1      22588 0.8437804         19
    ## 5         Tongue Ranger District         2       4182 0.1562196          2
    ##   P1POINTCNTFOR
    ## 1             8
    ## 2             3
    ## 3             9
    ## 4            16
    ## 5             1

    # Variable names
    stratdat.bhdist$unitvar        # Estimation unit variable

output

    ## [1] "DISTRICTNA"

    stratdat.bhdist$strvar         # Strata variable

output

    ## [1] "STRATUMCD"

    stratdat.bhdist$areavar        # Area variable

output

    ## [1] "ACRES_GIS"

### GB Module

- modGBpop()
- modGBarea()
- modGBtree()
- modGBratio()

### modGBpop()

`FIESTA`’s population functions (`mod*pop`) check input data and perform population-level calculations, such as: summing number of sampled plots; adjusting for partial nonresponse; and standardizing auxiliary data. These functions are specific to each `FIESTA` module and are run prior to or within a module for any population of interest.

For `FIESTA`’s GB Module, the `modGBpop` function calculates and outputs: number of plots, adjustment factors, and an expansion factor by strata. The outputs are similar to data found in FIADB’s pop_stratum table. The output from `modGBpop` can be used for one or more estimates from `modGBarea`, `modGBtree`, or `modGBratio` functions.

#### POP1: FIADB POPULATION - Get population data for post-stratified area and tree estimates for Wyoming

View Example

In this example, we use the sample Wyoming data (2013 Evaluation) stored in `FIESTA` to generate population data for the GB module. We check this output with the FIADB pop_stratum table from FIA DataMart for 561301 Evalid, using the `FIESTA::DBqryCSV` function.

    GBpopdat <- modGBpop(popTabs = list(cond = FIESTA::WYcond,          # FIA plot/condition data
                                        tree = FIESTA::WYtree,          # FIA tree data
                                        seed = FIESTA::WYseed),         # FIA seedling data
                         popTabIDs = list(cond = "PLT_CN"),             # unique ID of plot in cond
                         pltassgn = FIESTA::WYpltassgn,                 # plot assignments
                         pltassgnid = "CN",                             # unique ID of plot in pltassgn
                         pjoinid = "PLT_CN",                            # plot id to join to pltassgn
                         unitarea = FIESTA::WYunitarea,                 # area by estimation units
                         unitvar = "ESTN_UNIT",                         # name of estimation unit variable
                         strata = TRUE,                                 # if using post-stratification
                         stratalut = FIESTA::WYstratalut,               # strata classes and pixels counts
                         strata_opts = strata_options(getwt = TRUE))    # strata options

To get the names of the list components associated with the output of our call of `modGBpop`, we run the following code:

    names(GBpopdat)

output

    ##  [1] "module"        "popType"       "pltidsadj"     "pltcondx"
    ##  [5] "pltcondflds"   "pjoinid"       "cuniqueid"     "condid"
    ##  [9] "ACI"           "areawt"        "areawt2"       "adjcase"
    ## [13] "dbqueries"     "dbqueriesWITH" "pltassgnx"     "pltassgnid"
    ## [17] "unitarea"      "areavar"       "areaunits"     "unitvar"
    ## [21] "unitvars"      "unitltmin"     "strata"        "stratalut"
    ## [25] "strvar"        "strwtvar"      "plotsampcnt"   "condsampcnt"
    ## [29] "states"        "invyrs"        "adj"           "P2POINTCNT"
    ## [33] "plotunitcnt"   "treex"         "tuniqueid"     "seedx"
    ## [37] "adjfactors"    "adjvarlst"     "popdatindb"

From this list outputted by `GBpopdat` we can access many things. Some examples include the number of plots by plot status (`plotsampcnt`), the number of conditions by condition status (`condsampcnt`), the number of plots and adjustment factors by strata (`stratalut`), and the adjustment factors added to the plot-level, tree-level, and seedling data (`pltidsadj`, `treex`, and `seedx`). These objects can be seen below:

    # Number of plots by plot status
    GBpopdat$plotsampcnt

output

    ## data frame with 0 columns and 0 rows

    # Number of conditions by condition status
    GBpopdat$condsampcnt

output

    ##    COND_STATUS_NM COND_STATUS_CD NBRCONDS
    ## 1  Nonforest land              2     2590
    ## 2     Forest land              1      590
    ## 3 Noncensus water              3       10
    ## 4      Nonsampled              5       14
    ## 5    Census water              4       20

    # Number of plots and adjustment factors by strata
    GBpopdat$stratalut

output

    ## Key: <ESTN_UNIT, STRATUMCD>
    ##     ESTN_UNIT STRATUMCD P1POINTCNT n.total n.strata      strwt
    ##        <fctr>     <num>      <num>   <int>    <int>      <num>
    ##  1:         1         1      30603     133       17 0.17138393
    ##  2:         1         2     147961     133      116 0.82861607
    ##  3:         3         1      15896      98       12 0.12145384
    ##  4:         3         2     114985      98       86 0.87854616
    ##  5:         5         2     198981     152      152 1.00000000
    ##  6:         7         1      50473     245       35 0.15293736
    ##  7:         7         2     279551     245      210 0.84706264
    ##  8:         9         1      13946     133       16 0.07891401
    ##  9:         9         2     162778     133      117 0.92108599
    ## 10:        11         1      34965      85       28 0.29395692
    ## 11:        11         2      83981      85       57 0.70604308
    ## 12:        13         1      60592     290       48 0.15780564
    ## 13:        13         2     323374     290      242 0.84219436
    ## 14:        15         2      92483      70       70 1.00000000
    ## 15:        17         2      83149      58       58 1.00000000
    ## 16:        19         1      24652     128       18 0.14250699
    ## 17:        19         2     148336     128      110 0.85749301
    ## 18:        21         2     111389      86       86 1.00000000
    ## 19:        23         1      49359     132       35 0.29129978
    ## 20:        23         2     120085     132       97 0.70870022
    ## 21:        25         2     222755     175      175 1.00000000
    ## 22:        27         2     108902      79       79 1.00000000
    ## 23:        29         1     140049     216      100 0.48499131
    ## 24:        29         2     148717     216      116 0.51500869
    ## 25:        31         2      87474      64       64 1.00000000
    ## 26:        33         1      24037      82       18 0.22951399
    ## 27:        33         2      80693      82       64 0.77048601
    ## 28:        35         1      55527     158       44 0.27151107
    ## 29:        35         2     148984     158      114 0.72848893
    ## 30:        37         2     434729     339      339 1.00000000
    ## 31:        39         1     128994     125       98 0.73730659
    ## 32:        39         2      45959     125       27 0.26269341
    ## 33:        41         2      86508      63       63 1.00000000
    ## 34:        43         2      92938      63       63 1.00000000
    ## 35:        45         2      99461      73       73 1.00000000
    ##     ESTN_UNIT STRATUMCD P1POINTCNT n.total n.strata      strwt

    # Adjustment factors added to plot-level data
    head(GBpopdat$pltidsadj)

output

    ## Key: <CN>
    ##                CN ADJ_FACTOR_COND ADJ_FACTOR_SUBP ADJ_FACTOR_MACR
    ##            <char>           <num>           <num>           <int>
    ## 1: 40404728010690        1.000000        1.000000               0
    ## 2: 40404729010690        1.000000        1.000000               0
    ## 3: 40404730010690        1.014925        1.014925               0
    ## 4: 40404731010690        1.000000        1.000000               0
    ## 5: 40404733010690        1.000000        1.000000               0
    ## 6: 40404734010690        1.000000        1.000000               0
    ##    ADJ_FACTOR_MICR
    ##              <num>
    ## 1:        1.000000
    ## 2:        1.000000
    ## 3:        1.014925
    ## 4:        1.000000
    ## 5:        1.000000
    ## 6:        1.000000

    # Adjustment factors added to tree data
    head(GBpopdat$treex)

output

    ## Key: <PLT_CN, CONDID, SUBP, TREE>
    ##            PLT_CN CONDID  SUBP  TREE STATUSCD  SPCD SPGRPCD   DIA    HT
    ##            <char>  <num> <num> <num>    <num> <num>   <num> <num> <num>
    ## 1: 40404729010690      1     1     1        2   113      24   7.7    18
    ## 2: 40404729010690      1     1     2        1    66      23  10.8    14
    ## 3: 40404729010690      1     1     3        2   113      24   5.2    23
    ## 4: 40404729010690      1     1     4        1   113      24   5.2    18
    ## 5: 40404729010690      1     3     1        1   113      24   8.8    21
    ## 6: 40404729010690      1     4     1        1   113      24   8.9    28
    ##    TREECLCD AGENTCD STANDING_DEAD_CD VOLCFNET VOLCFGRS VOLBFNET TPA_UNADJ
    ##       <num>   <num>            <num>    <num>    <num>    <num>     <num>
    ## 1:        3      10                1 1.001201 1.820365       NA  6.018046
    ## 2:        3      NA               NA       NA       NA       NA  6.018046
    ## 3:        3      10                1 0.466414 0.848025       NA  6.018046
    ## 4:        2      NA               NA 0.630180 0.630180       NA  6.018046
    ## 5:        3      NA               NA 2.491559 2.931246       NA  6.018046
    ## 6:        3      NA               NA 3.824139 4.202350       NA  6.018046
    ##    DRYBIO_AG CARBON_AG tadjfac
    ##        <num>     <num>   <num>
    ## 1:  68.32740  34.43701       1
    ## 2: 128.28703  61.19291       1
    ## 3:  40.24585  20.28391       1
    ## 4:  40.61207  19.49379       1
    ## 5: 144.25115  69.24055       1
    ## 6: 182.53588  87.61722       1

    # Adjustment factors added to seedling data
    head(GBpopdat$seedx)

output

    ## Key: <PLT_CN, CONDID, SUBP>
    ##            PLT_CN  SUBP CONDID  SPCD SPGRPCD  TPA_UNADJ TREECOUNT
    ##            <char> <num>  <num> <num>   <num>      <num>     <num>
    ## 1: 40404729010690     2      1   113      24   74.96528         1
    ## 2: 40404730010690     2      1   202      10  224.89585         3
    ## 3: 40404738010690     2      1   746      44 2323.92376        31
    ## 4: 40404738010690     4      1    19      12   74.96528         1
    ## 5: 40404738010690     4      1   113      24   74.96528         1
    ## 6: 40404742010690     4      1   746      44 1124.47924        15
    ##    TREECOUNT_CALC  tadjfac
    ##             <num>    <num>
    ## 1:              1 1.000000
    ## 2:              3 1.014925
    ## 3:             31 1.014925
    ## 4:              1 1.014925
    ## 5:              1 1.014925
    ## 6:             15 1.014925

One may also want to compare `FIESTA` output with FIADB pop_stratum table for WY in the 2013 evaluation to check for consistency. The can be done as follows:

    qry <- "SELECT ESTN_UNIT, STRATUMCD, P1POINTCNT, P2POINTCNT,
                   ADJ_FACTOR_MACR, ADJ_FACTOR_SUBP, ADJ_FACTOR_MICR
            FROM POP_STRATUM
            WHERE EVALID = 561301
            ORDER BY ESTN_UNIT, STRATUMCD"

    pop_stratum <- DBqryCSV(qry,
                            states="Wyoming",
                            sqltables="POP_STRATUM")

    head(pop_stratum)

output

    ##   ESTN_UNIT STRATUMCD P1POINTCNT P2POINTCNT ADJ_FACTOR_MACR ADJ_FACTOR_SUBP
    ## 1         1         1      30603         17               0        1.014925
    ## 2         1         2     147961        116               0        1.000000
    ## 3         3         1      15896         12               0        1.000000
    ## 4         3         2     114985         86               0        1.008798
    ## 5         5         2     198981        152               0        1.000000
    ## 6         7         1      50473         35               0        1.000000
    ##   ADJ_FACTOR_MICR
    ## 1        1.014925
    ## 2        1.000000
    ## 3        1.000000
    ## 4        1.008798
    ## 5        1.000000
    ## 6        1.000000

    head(GBpopdat$stratalut)

output

    ## Key: <ESTN_UNIT, STRATUMCD>
    ##    ESTN_UNIT STRATUMCD P1POINTCNT n.total n.strata     strwt
    ##       <fctr>     <num>      <num>   <int>    <int>     <num>
    ## 1:         1         1      30603     133       17 0.1713839
    ## 2:         1         2     147961     133      116 0.8286161
    ## 3:         3         1      15896      98       12 0.1214538
    ## 4:         3         2     114985      98       86 0.8785462
    ## 5:         5         2     198981     152      152 1.0000000
    ## 6:         7         1      50473     245       35 0.1529374

#### POP2: CUSTOM POPULATION - Get population data for post-stratified area and tree estimates for the Bighorn National Forest

View Example

In this example, we use the sample WY plot data (2013 Evaluation) in FIESTA and output from `spGetStrata` to generate population data for the Bighorn National Forest. Here, we have only one estimation unit within the population of interest (Bighorn National Forest), therefore strata and pixel counts are summarized to the population.

If the `FIESTA::spGetStrata` function is used to obtain stratification data, the output list object can be input directly into `modGBpop` through the `stratdat` parameter.

    GBpopdat.bh <- modGBpop(popTabs = popTables(plt = WYplt,
                                                cond = WYcond,
                                                tree = WYtree,
                                                seed = WYseed),
                            stratdat = stratdat.bh)

    # Get names of output list components
    names(GBpopdat.bh)

output

    ##  [1] "module"        "popType"       "pltidsadj"     "pltcondx"
    ##  [5] "pltcondflds"   "pjoinid"       "cuniqueid"     "condid"
    ##  [9] "ACI"           "areawt"        "areawt2"       "adjcase"
    ## [13] "dbqueries"     "dbqueriesWITH" "pltassgnx"     "pltassgnid"
    ## [17] "unitarea"      "areavar"       "areaunits"     "unitvar"
    ## [21] "unitvars"      "unitltmin"     "strata"        "stratalut"
    ## [25] "strvar"        "strwtvar"      "plotsampcnt"   "condsampcnt"
    ## [29] "states"        "invyrs"        "adj"           "P2POINTCNT"
    ## [33] "plotunitcnt"   "treex"         "tuniqueid"     "seedx"
    ## [37] "adjfactors"    "adjvarlst"     "popdatindb"

The utilization of the `stratdat` parameter shown above is simply a shortcut that is available when `spGetStrata()` was used. Alternatively, the data can be input through individual parameters as shown below.

    ## Using output as individual parameter inputs
    GBpopdat.bh <- modGBpop(popTabs = popTables(plt = WYplt,
                                                cond = WYcond,
                                                tree = WYtree,
                                                seed = WYseed),
                            popTabIDs = popTableIDs(plt = "CN"),
                            pltassgn = stratdat.bh$pltassgn,
                            pltassgnid = "CN",
                            unitvar = stratdat.bh$unitvar,
                            unitarea = stratdat.bh$unitarea,
                            areavar = stratdat.bh$areavar,
                            strata = TRUE,
                            stratalut  =stratdat.bh$stratalut,
                            strvar = stratdat.bh$strvar)

    ## Get names of output list components
    names(GBpopdat.bh)

output

    ##  [1] "module"        "popType"       "pltidsadj"     "pltcondx"
    ##  [5] "pltcondflds"   "pjoinid"       "cuniqueid"     "condid"
    ##  [9] "ACI"           "areawt"        "areawt2"       "adjcase"
    ## [13] "dbqueries"     "dbqueriesWITH" "pltassgnx"     "pltassgnid"
    ## [17] "unitarea"      "areavar"       "areaunits"     "unitvar"
    ## [21] "unitvars"      "unitltmin"     "strata"        "stratalut"
    ## [25] "strvar"        "strwtvar"      "plotsampcnt"   "condsampcnt"
    ## [29] "states"        "invyrs"        "adj"           "P2POINTCNT"
    ## [33] "plotunitcnt"   "treex"         "tuniqueid"     "seedx"
    ## [37] "adjfactors"    "adjvarlst"     "popdatindb"

#### POP3: CUSTOM SUB-POPULATIONS - Get sub-population data for area and tree estimates for the Bighorn National Forest Districts, using post-stratification

View Example

In this example, we use the sample Wyoming plot data (2013 Evaluation) stored in FIESTA and output from `spGetStrata` to generate sub-population data for Bighorn National Forest Districts. Here, we have more than one estimation unit (i.e., sub-population) within the population of interest (i.e., Bighorn National Forest Districts), therefore strata and pixel counts are summarized by each District within the population.

    # Bighorn National Forest District

    # Using output list from spGetStrata()
    GBpopdat.bhdist <- modGBpop(popTabs = popTables(plt = WYplt,
                                                    cond = WYcond,
                                                    tree = WYtree,
                                                    seed = WYseed),
                                stratdat = stratdat.bhdist)

    ## Get names of output list components
    names(GBpopdat.bhdist)

output

    ##  [1] "module"        "popType"       "pltidsadj"     "pltcondx"
    ##  [5] "pltcondflds"   "pjoinid"       "cuniqueid"     "condid"
    ##  [9] "ACI"           "areawt"        "areawt2"       "adjcase"
    ## [13] "dbqueries"     "dbqueriesWITH" "pltassgnx"     "pltassgnid"
    ## [17] "unitarea"      "areavar"       "areaunits"     "unitvar"
    ## [21] "unitvars"      "unitltmin"     "strata"        "stratalut"
    ## [25] "strvar"        "strwtvar"      "plotsampcnt"   "condsampcnt"
    ## [29] "states"        "invyrs"        "adj"           "P2POINTCNT"
    ## [33] "plotunitcnt"   "treex"         "tuniqueid"     "seedx"
    ## [37] "adjfactors"    "adjvarlst"     "popdatindb"

#### POP4: FIADB POPULATION - Get population data for area and tree estimates for Rhode Island, using post-stratification, with data stored in a SQLite database

View Example

In this example, we use the sample Rhode Island data (441901 Evaluation) stored in a SQLite database as external data in `FIESTA`. Data were extracted from the FIA database on June 6, 2022. All output can be compared with output from other FIA tools.

First, let’s look at the SQLite database. Use the DBI package explore the contents.

    SQLitefn <- system.file("extdata", "FIA_data/RIdat_eval2019.db", package="FIESTA")

    conn <- DBI::dbConnect(RSQLite::SQLite(), SQLitefn)
    DBI::dbListTables(conn)
    DBI::dbDisconnect(conn)

    GBpopdat.RI <- modGBpop(popTabs = popTables(plt = "plot",
                                                cond = "cond",
                                                tree = "tree",
                                                seed = "seed"),
                            dsn = SQLitefn,
                            pltassgn = "pop_plot_stratum_assgn",
                            stratalut = "pop_stratum",
                            unitarea = "pop_estn_unit",
                            unitvar = "ESTN_UNIT",
                            areavar = "AREA_USED",
                            strata_opts = strata_options(getwt = TRUE,
                                                         getwtvar = "P1POINTCNT"))

    names(GBpopdat.RI)

    # Strata-level population data, including number of plots and adjustment factors
    GBpopdat.RI$stratalut

### modGBarea()

`FIESTA`‘s `modGBarea` function generates acre estimates. Calculations are based on Scott et al. 2015 (’Green-Book’) for mapped forest inventory plots. The non-ratio estimator for estimating area by stratum and domain is used. Plots that are totally nonsampled are excluded from the estimation dataset. Next, an adjustment factor is calculated by strata to adjust for nonsampled (nonresponse) conditions that have proportion less than 1. The attribute is the proportion of the plot which is divided by the adjustment factor, and averaged by stratum. Strata means are combined using the strata weights and then expanded to acres using the total land area in the population.

If there are more than one estimation unit (i.e., subpopulation) within the population, estimates are generated by estimation unit. If `sumunits=TRUE`, the estimates and percent standard errors returned are a sum combination of all estimation units. If `rawdata=TRUE`, the raw data returned will include estimates by estimation unit.

Parameters defined in the following examples are organized by category: population data (pop); estimation information (est); and output details (out).

#### POP1: 1.1 Area of forest land, Wyoming, 2011-2013

View Example

Using the `modGBarea` function we generate estimates by estimation unit (i.e., ESTN_UNIT) and sum to population (i.e., WY). `FIESTA` then returns raw data for area of forest land, Wyoming, 2011-2013 (sum estimation units).

The following estimates match output from EVALIDator using the WY 2013 Evaluation.

    area1.1 <- modGBarea(GBpopdat = GBpopdat,
                         landarea = "FOREST",
                         sumunits = TRUE)

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area1.1)

output

    ## [1] "est"     "raw"     "statecd" "states"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from out outputted list:

    area1.1$est

output

    ##   TOTAL Estimate Percent Sampling Error
    ## 1 Total 10455772                   2.37

We can also look at raw data and estimates, as shown below:

    raw1.1 <- area1.1$raw        # extract raw data list object from output
    names(raw1.1)

output

    ##  [1] "unit_totest" "totest"      "domdat"      "domdatqry"   "module"
    ##  [6] "esttype"     "popType"     "GBmethod"    "rowvar"      "colvar"
    ## [11] "areaunits"

    head(raw1.1$unit_totest)    # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT       nhat     nhat.var NBRPLT.gt0 AREAUSED       est    est.var
    ## 1         1 0.20844715 0.0004226522         24  2757613 574816.56 3214028956
    ## 2        11 0.25385133 0.0005556666         26  1837124 466356.38 1875388511
    ## 3        13 0.16668783 0.0001399449         53  5930088 988473.50 4921292746
    ## 4        15 0.02857143 0.0004022479          2  1428579  40816.54  820922693
    ## 5        17 0.09913793 0.0013966899          8  1283969 127290.03 2302550041
    ## 6        19 0.14724597 0.0005090321         22  2671802 393412.08 3633738980
    ##     est.se     est.cv       pse   CI99left CI99right  CI95left  CI95right
    ## 1 56692.41 0.09862695  9.862695 428786.600  720846.5 463701.49  685931.64
    ## 2 43305.76 0.09285979  9.285979 354808.144  577904.6 381478.66  551234.10
    ## 3 70151.93 0.07096996  7.096996 807774.111 1169172.9 850978.25 1125968.75
    ## 4 28651.75 0.70196412 70.196412      0.000  114618.6      0.00   96972.94
    ## 5 47984.89 0.37697292 37.697292   3689.134  250890.9  33241.37  221338.69
    ## 6 60280.50 0.15322484 15.322484 238139.793  548684.4 275264.46  511559.69
    ##    CI68left CI68right NBRPLT
    ## 1 518438.35  631194.8    133
    ## 2 423290.63  509422.1     85
    ## 3 918710.36 1058236.6    290
    ## 4  12323.59   69309.5     70
    ## 5  79571.07  175009.0     58
    ## 6 333465.66  453358.5    128

    raw1.1$totest               # estimates for population (i.e., WY)

output

    ##   TOTAL      est     est.var NBRPLT.gt0 AREAUSED   est.se    est.cv     pse
    ## 1     1 10455771 61379281561        556 62600430 247748.4 0.0236949 2.36949
    ##   CI99left CI99right CI95left CI95right CI68left CI68right NBRPLT
    ## 1  9817614  11093929  9970194  10941349 10209396  10702147   3047

#### POP1: 1.2 Area by forest type on forest land, Wyoming, 2011-2013

View Example

In this example, we look at adding rows to the output and include `returntitle=TRUE` to return title information. We use the variable `rowvar` to specify a domain whose levels we want to calculate estimates by.

    ## Area of forest land by forest type, Wyoming, 2011-2013
    area1.2 <- modGBarea(GBpopdat = GBpopdat,
                         landarea = "FOREST",         # est - forest land filter
                         rowvar = "FORTYPCD",         # est - row domain
                         sumunits = TRUE,             # est - sum estimation units to population
                         returntitle = TRUE)          # out - return title information

Again, we can look at the contents of the output list. The output now includes titlelst, a list of associated titles.

    names(area1.2)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"

And the estimates:

    ## Estimate and percent sampling error of estimate
    area1.2$est

output

    ##    Forest type   Estimate Percent Sampling Error
    ## 1          182   632481.7                  17.28
    ## 2          184   339749.8                  23.85
    ## 3          185    14854.7                    100
    ## 4          201     881189                  14.21
    ## 5          221   889542.8                  12.82
    ## 6          265   467196.7                  19.99
    ## 7          266  1521792.8                  10.41
    ## 8          268   950041.6                  13.55
    ## 9          269      19120                 101.99
    ## 10         281  2483772.2                   7.79
    ## 11         366   236355.9                  28.47
    ## 12         367   362502.8                  22.36
    ## 13         509    95082.1                  45.29
    ## 14         517      19287                 107.34
    ## 15         703    87991.9                  44.35
    ## 16         706    10593.4                    100
    ## 17         901     617017                  17.39
    ## 18         999   827200.1                  14.62
    ## 19        <NA>         --                     --
    ## 20       Total 10455771.5                   2.37

Along with raw data and titles:

    raw1.2 <- area1.2$raw      # extract raw data list object from output
    names(raw1.2)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "domdat"
    ##  [6] "domdatqry"   "module"      "esttype"     "popType"     "GBmethod"
    ## [11] "rowvar"      "colvar"      "areaunits"

    head(raw1.2$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT       nhat     nhat.var NBRPLT.gt0 AREAUSED       est    est.var
    ## 1         1 0.20844715 0.0004226522         24  2757613 574816.56 3214028956
    ## 2        11 0.25385133 0.0005556666         26  1837124 466356.38 1875388511
    ## 3        13 0.16668783 0.0001399449         53  5930088 988473.50 4921292746
    ## 4        15 0.02857143 0.0004022479          2  1428579  40816.54  820922693
    ## 5        17 0.09913793 0.0013966899          8  1283969 127290.03 2302550041
    ## 6        19 0.14724597 0.0005090321         22  2671802 393412.08 3633738980
    ##     est.se     est.cv       pse   CI99left CI99right  CI95left  CI95right
    ## 1 56692.41 0.09862695  9.862695 428786.600  720846.5 463701.49  685931.64
    ## 2 43305.76 0.09285979  9.285979 354808.144  577904.6 381478.66  551234.10
    ## 3 70151.93 0.07096996  7.096996 807774.111 1169172.9 850978.25 1125968.75
    ## 4 28651.75 0.70196412 70.196412      0.000  114618.6      0.00   96972.94
    ## 5 47984.89 0.37697292 37.697292   3689.134  250890.9  33241.37  221338.69
    ## 6 60280.50 0.15322484 15.322484 238139.793  548684.4 275264.46  511559.69
    ##    CI68left CI68right NBRPLT
    ## 1 518438.35  631194.8    133
    ## 2 423290.63  509422.1     85
    ## 3 918710.36 1058236.6    290
    ## 4  12323.59   69309.5     70
    ## 5  79571.07  175009.0     58
    ## 6 333465.66  453358.5    128

    raw1.2$totest            # estimates for population (i.e., WY)

output

    ##   TOTAL      est     est.var NBRPLT.gt0 AREAUSED   est.se    est.cv     pse
    ## 1     1 10455771 61379281561        556 62600430 247748.4 0.0236949 2.36949
    ##   CI99left CI99right CI95left CI95right CI68left CI68right NBRPLT
    ## 1  9817614  11093929  9970194  10941349 10209396  10702147   3047

    head(raw1.2$unit_rowest) # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT Forest type       nhat     nhat.var NBRPLT.gt0 AREAUSED       est
    ## 1         1         182 0.01428648 0.0001066487          2  2757613  39396.59
    ## 2         1         201 0.01023188 0.0000809180          1  2757613  28215.56
    ## 3         1         221 0.03239380 0.0002137597          4  2757613  89329.58
    ## 4         1         266 0.04092751 0.0002629835          4  2757613 112862.22
    ## 5         1         268 0.01023188 0.0000809180          1  2757613  28215.56
    ## 6         1         281 0.03854903 0.0002367898          4  2757613 106303.32
    ##      est.var   est.se    est.cv      pse CI99left CI99right CI95left CI95right
    ## 1  811002553 28478.11 0.7228571 72.28571        0 112751.34     0.00  95212.66
    ## 2  615335251 24805.95 0.8791587 87.91587        0  92111.45     0.00  76834.33
    ## 3 1625520387 40317.74 0.4513370 45.13370        0 193181.20 10308.25 168350.90
    ## 4 1999839565 44719.57 0.3962315 39.62315        0 228052.19 25213.48 200510.96
    ## 5  615335251 24805.95 0.8791587 87.91587        0  92111.45     0.00  76834.33
    ## 6 1800651626 42434.09 0.3991793 39.91793        0 215606.28 23134.04 189472.60
    ##    CI68left CI68right
    ## 1 11076.316  67716.87
    ## 2  3547.081  52884.03
    ## 3 49235.278 129423.87
    ## 4 68390.497 157333.95
    ## 5  3547.081  52884.03
    ## 6 64104.407 148502.23

    head(raw1.2$rowest)      # estimates by row for population (i.e., WY)

output

    ##   Forest type       est     est.var NBRPLT.gt0    est.se    est.cv       pse
    ## 1         182 632481.70 11940316027         36 109271.75 0.1727667  17.27667
    ## 2         184 339749.83  6565509067         18  81027.83 0.2384926  23.84926
    ## 3         185  14854.69   220661757          1  14854.69 1.0000000 100.00000
    ## 4         201 881188.96 15674351133         47 125197.25 0.1420776  14.20776
    ## 5         221 889542.77 13008784420         51 114056.06 0.1282187  12.82187
    ## 6         265 467196.69  8723584461         27  93400.13 0.1999161  19.99161
    ##   CI99left  CI99right CI95left  CI95right     CI68left  CI68right
    ## 1 351016.3  913947.08 418313.0  846650.40 523815.54425  741147.86
    ## 2 131036.0  548463.69 180938.2  498561.46 259171.07046  420328.60
    ## 3      0.0   53117.83      0.0   43969.34     82.32642   29627.05
    ## 4 558702.2 1203675.70 635806.9 1126571.06 756685.57030 1005692.35
    ## 5 595753.8 1183331.71 665997.0 1113088.54 776118.82419 1002966.72
    ## 6 226613.9  707779.49 284135.8  650257.59 374314.19798  560079.19

    ## Titles (list object) for estimate
    titlelst1.2 <- area1.2$titlelst
    names(titlelst1.2)

output

    ## [1] "title.estpse"  "title.unitvar" "title.ref"     "outfn.estpse"
    ## [5] "outfn.rawdat"  "outfn.param"   "title.rowvar"  "title.row"
    ## [9] "title.unit"

    titlelst1.2

output

    ## $title.estpse
    ## [1] "Area, in acres, and percent sampling error on forest land by forest type"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] ""
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPCD_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPCD_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPCD_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest type"
    ##
    ## $title.unit
    ## [1] "acres"

#### POP1: 1.3 Area by forest type and stand-size class on forest land, Wyoming, 2011-2013

View Example

In this example, we look at generating estimates by multiple domains. In order to enhance the readability of the output we use FIA names as labels. We also output estimates and percent standard error in the same cell with the `allin1` argument in `table_options` and save data to an outfolder with the `outfolder` argument in `savedata_options`.

    ## Area of forest land by forest type and stand-size class, Wyoming, 2011-2013

    area1.3 <- modGBarea(GBpopdat = GBpopdat,                                     # pop - population calculations for WY, post-stratification
                         landarea = "FOREST",                                     # est - forest land filter
                         rowvar = "FORTYPCD",                                     # est - row domain
                         colvar = "STDSZCD",                                      # est - column domain
                         sumunits = TRUE,                                         # est - sum estimation units to population
                         savedata = TRUE,                                         # out - save data to outfolder
                         returntitle = TRUE,                                      # out - return title information
                         table_opts = table_options(row.FIAname = TRUE,           # table - row domain names
                                                    col.FIAname = TRUE,           # table - column domain names
                                                    allin1 = TRUE),               # table - return output with est(pse)
                         savedata_opts = savedata_options(outfolder = outfolder,  # save - outfolder for saving data
                                                          outfn.pre = "WY"))      # save - prefix for output files

We can again look at the output list, estimates, raw data, and titles:

    # Look at output list
    names(area1.3)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"

    # Estimate and percent sampling error of estimate
    head(area1.3$est)

output

    ##                 Forest type     Large diameter    Medium diameter
    ## 1    Rocky Mountain juniper 477,452.6 ( 19.91)  63,177.6 ( 51.17)
    ## 2          Juniper woodland 317,612.4 ( 24.54)        -- (    --)
    ## 3 Pinyon / juniper woodland  14,854.7 (100.00)        -- (    --)
    ## 4               Douglas-fir 589,713.2 ( 17.41) 111,576.9 ( 41.36)
    ## 5            Ponderosa pine 786,751.8 ( 13.52)  46,720.3 ( 60.51)
    ## 6          Engelmann spruce 332,893.7 ( 24.00)  68,400.6 ( 54.85)
    ##       Small diameter  Nonstocked              Total
    ## 1  91,851.5 ( 46.14) -- (    --) 617,017.0 ( 17.39)
    ## 2  22,137.4 (100.00) -- (    --)  19,120.0 (101.99)
    ## 3        -- (    --) -- (    --)  95,082.1 ( 45.29)
    ## 4 179,898.8 ( 31.91) -- (    --)  87,991.9 ( 44.35)
    ## 5  56,070.7 ( 58.39) -- (    --) 881,189.0 ( 14.21)
    ## 6  65,902.4 ( 50.88) -- (    --)  19,287.0 (107.34)

    # Raw data (list object) for estimate
    raw1.3 <- area1.3$raw      # extract raw data list object from output
    names(raw1.3)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "unit_colest"
    ##  [6] "colest"      "unit_grpest" "grpest"      "domdat"      "domdatqry"
    ## [11] "module"      "esttype"     "popType"     "GBmethod"    "rowvar"
    ## [16] "colvar"      "areaunits"

    head(raw1.3$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT       nhat     nhat.var NBRPLT.gt0 AREAUSED       est    est.var
    ## 1         1 0.20844715 0.0004226522         24  2757613 574816.56 3214028956
    ## 2        11 0.25385133 0.0005556666         26  1837124 466356.38 1875388511
    ## 3        13 0.16668783 0.0001399449         53  5930088 988473.50 4921292746
    ## 4        15 0.02857143 0.0004022479          2  1428579  40816.54  820922693
    ## 5        17 0.09913793 0.0013966899          8  1283969 127290.03 2302550041
    ## 6        19 0.14724597 0.0005090321         22  2671802 393412.08 3633738980
    ##     est.se     est.cv       pse   CI99left CI99right  CI95left  CI95right
    ## 1 56692.41 0.09862695  9.862695 428786.600  720846.5 463701.49  685931.64
    ## 2 43305.76 0.09285979  9.285979 354808.144  577904.6 381478.66  551234.10
    ## 3 70151.93 0.07096996  7.096996 807774.111 1169172.9 850978.25 1125968.75
    ## 4 28651.75 0.70196412 70.196412      0.000  114618.6      0.00   96972.94
    ## 5 47984.89 0.37697292 37.697292   3689.134  250890.9  33241.37  221338.69
    ## 6 60280.50 0.15322484 15.322484 238139.793  548684.4 275264.46  511559.69
    ##    CI68left CI68right NBRPLT
    ## 1 518438.35  631194.8    133
    ## 2 423290.63  509422.1     85
    ## 3 918710.36 1058236.6    290
    ## 4  12323.59   69309.5     70
    ## 5  79571.07  175009.0     58
    ## 6 333465.66  453358.5    128

    head(raw1.3$totest)      # estimates for population (i.e., WY)

output

    ##   TOTAL      est     est.var NBRPLT.gt0 AREAUSED   est.se    est.cv     pse
    ## 1     1 10455771 61379281561        556 62600430 247748.4 0.0236949 2.36949
    ##   CI99left CI99right CI95left CI95right CI68left CI68right NBRPLT
    ## 1  9817614  11093929  9970194  10941349 10209396  10702147   3047

    head(raw1.3$unit_rowest) # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT FORTYPCD       nhat     nhat.var NBRPLT.gt0
    ## 1         1      182 0.01428648 0.0001066487          2
    ## 2         1      201 0.01023188 0.0000809180          1
    ## 3         1      221 0.03239380 0.0002137597          4
    ## 4         1      266 0.04092751 0.0002629835          4
    ## 5         1      268 0.01023188 0.0000809180          1
    ## 6         1      281 0.03854903 0.0002367898          4
    ##                        Forest type AREAUSED       est    est.var   est.se
    ## 1           Rocky Mountain juniper  2757613  39396.59  811002553 28478.11
    ## 2                      Douglas-fir  2757613  28215.56  615335251 24805.95
    ## 3                   Ponderosa pine  2757613  89329.58 1625520387 40317.74
    ## 4 Engelmann spruce / subalpine fir  2757613 112862.22 1999839565 44719.57
    ## 5                    Subalpine fir  2757613  28215.56  615335251 24805.95
    ## 6                   Lodgepole pine  2757613 106303.32 1800651626 42434.09
    ##      est.cv      pse CI99left CI99right CI95left CI95right  CI68left CI68right
    ## 1 0.7228571 72.28571        0 112751.34     0.00  95212.66 11076.316  67716.87
    ## 2 0.8791587 87.91587        0  92111.45     0.00  76834.33  3547.081  52884.03
    ## 3 0.4513370 45.13370        0 193181.20 10308.25 168350.90 49235.278 129423.87
    ## 4 0.3962315 39.62315        0 228052.19 25213.48 200510.96 68390.497 157333.95
    ## 5 0.8791587 87.91587        0  92111.45     0.00  76834.33  3547.081  52884.03
    ## 6 0.3991793 39.91793        0 215606.28 23134.04 189472.60 64104.407 148502.23

    head(raw1.3$rowest)      # estimates by row for population (i.e., WY)

output

    ##                 Forest type       est     est.var NBRPLT.gt0    est.se
    ## 1    Rocky Mountain juniper 632481.70 11940316027         36 109271.75
    ## 2          Juniper woodland 339749.83  6565509067         18  81027.83
    ## 3 Pinyon / juniper woodland  14854.69   220661757          1  14854.69
    ## 4               Douglas-fir 881188.96 15674351133         47 125197.25
    ## 5            Ponderosa pine 889542.77 13008784420         51 114056.06
    ## 6          Engelmann spruce 467196.69  8723584461         27  93400.13
    ##      est.cv       pse CI99left  CI99right CI95left  CI95right     CI68left
    ## 1 0.1727667  17.27667 351016.3  913947.08 418313.0  846650.40 523815.54425
    ## 2 0.2384926  23.84926 131036.0  548463.69 180938.2  498561.46 259171.07046
    ## 3 1.0000000 100.00000      0.0   53117.83      0.0   43969.34     82.32642
    ## 4 0.1420776  14.20776 558702.2 1203675.70 635806.9 1126571.06 756685.57030
    ## 5 0.1282187  12.82187 595753.8 1183331.71 665997.0 1113088.54 776118.82419
    ## 6 0.1999161  19.99161 226613.9  707779.49 284135.8  650257.59 374314.19798
    ##    CI68right
    ## 1  741147.86
    ## 2  420328.60
    ## 3   29627.05
    ## 4 1005692.35
    ## 5 1002966.72
    ## 6  560079.19

    head(raw1.3$unit_colest) # estimates by column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT STDSZCD        nhat      nhat.var NBRPLT.gt0 Stand-size class
    ## 1         1       1 0.077375917 0.00045606257          9   Large diameter
    ## 2         1       2 0.061391258 0.00033378677          6  Medium diameter
    ## 3         1       3 0.059978760 0.00039535962          7   Small diameter
    ## 4         1       5 0.009701211 0.00005884949          2       Nonstocked
    ## 5        11       1 0.173224615 0.00079375869         18   Large diameter
    ## 6        11       2 0.036008259 0.00036134273          4  Medium diameter
    ##   AREAUSED       est    est.var   est.se    est.cv      pse  CI99left CI99right
    ## 1  2757613 213372.84 3468095619 58890.54 0.2759983 27.59983  61680.86  365064.8
    ## 2  2757613 169293.33 2538257910 50381.13 0.2975966 29.75966  39520.15  299066.5
    ## 3  2757613 165398.21 3006484369 54831.42 0.3315116 33.15116  24161.84  306634.6
    ## 4  2757613  26752.19  447516758 21154.59 0.7907613 79.07613      0.00   81242.8
    ## 5  1837124 318235.10 2678955113 51758.62 0.1626427 16.26427 184913.72  451556.5
    ## 6  1837124  66151.64 1219540611 34921.92 0.5279071 52.79071      0.00  156104.5
    ##    CI95left CI95right   CI68left CI68right
    ## 1  97949.50 328796.17 154808.675 271937.00
    ## 2  70548.14 268038.53 119191.424 219395.24
    ## 3  57930.61 272865.81 110870.673 219925.74
    ## 4      0.00  68214.42   5714.835  47789.54
    ## 5 216790.06 419680.13 266763.326 369706.87
    ## 6      0.00 134597.35  31423.257 100880.02

    head(raw1.3$colest)      # estimates by column for population (i.e., WY)

output

    ##   Stand-size class       est     est.var NBRPLT.gt0   est.se     est.cv
    ## 1   Large diameter 5344066.4 64292474661        297 253559.6 0.04744694
    ## 2  Medium diameter 1918907.4 30844444919        104 175625.9 0.09152389
    ## 3   Small diameter 2365597.5 39151933793        127 197868.5 0.08364419
    ## 4       Nonstocked  827200.1 14625718427         53 120936.8 0.14620022
    ##         pse  CI99left CI99right  CI95left CI95right  CI68left CI68right
    ## 1  4.744694 4690940.2   5997193 4847098.7   5841034 5091912.1 5596220.8
    ## 2  9.152389 1466525.2   2371290 1574687.1   2263128 1744254.9 2093560.0
    ## 3  8.364419 1855922.1   2875273 1977782.4   2753413 2168825.6 2562369.4
    ## 4 14.620022  515687.5   1138713  590168.3   1064232  706933.5  947466.7

    head(raw1.3$unit_grpest) # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT FORTYPCD STDSZCD        nhat      nhat.var NBRPLT.gt0
    ## 1         1      182       1 0.007143242 0.00005379211          1
    ## 2         1      182       3 0.007143242 0.00005379211          1
    ## 3         1      201       1 0.010231876 0.00008091800          1
    ## 4         1      221       1 0.032393804 0.00021375968          4
    ## 5         1      266       1 0.020463753 0.00015172126          2
    ## 6         1      266       2 0.020463753 0.00015172126          2
    ##   Stand-size class                      Forest type AREAUSED      est
    ## 1   Large diameter           Rocky Mountain juniper  2757613 19698.30
    ## 2   Small diameter           Rocky Mountain juniper  2757613 19698.30
    ## 3   Large diameter                      Douglas-fir  2757613 28215.56
    ## 4   Large diameter                   Ponderosa pine  2757613 89329.58
    ## 5   Large diameter Engelmann spruce / subalpine fir  2757613 56431.11
    ## 6  Medium diameter Engelmann spruce / subalpine fir  2757613 56431.11
    ##      est.var   est.se    est.cv       pse CI99left CI99right CI95left CI95right
    ## 1  409058305 20225.19 1.0267481 102.67481        0  71794.93     0.00  59338.94
    ## 2  409058305 20225.19 1.0267481 102.67481        0  71794.93     0.00  59338.94
    ## 3  615335251 24805.95 0.8791587  87.91587        0  92111.45     0.00  76834.33
    ## 4 1625520387 40317.74 0.4513370  45.13370        0 193181.20 10308.25 168350.90
    ## 5 1153753595 33966.95 0.6019188  60.19188        0 143924.17     0.00 123005.11
    ## 6 1153753595 33966.95 0.6019188  60.19188        0 143924.17     0.00 123005.11
    ##    CI68left CI68right
    ## 1     0.000  39811.40
    ## 2     0.000  39811.40
    ## 3  3547.081  52884.03
    ## 4 49235.278 129423.87
    ## 5 22652.411  90209.81
    ## 6 22652.411  90209.81

    head(raw1.3$grpest)      # estimates by row and column for population (i.e., WY)

output

    ##                 Forest type Stand-size class       est    est.var NBRPLT.gt0
    ## 1    Rocky Mountain juniper   Large diameter 477452.63 9035011628         28
    ## 2    Rocky Mountain juniper  Medium diameter  63177.58 1045063815          4
    ## 3    Rocky Mountain juniper   Small diameter  91851.49 1795723195          5
    ## 4          Juniper woodland   Large diameter 317612.44 6075444741         17
    ## 5          Juniper woodland   Small diameter  22137.40  490064326          1
    ## 6 Pinyon / juniper woodland   Large diameter  14854.69  220661757          1
    ##     est.se    est.cv       pse CI99left CI99right   CI95left CI95right
    ## 1 95052.68 0.1990829  19.90829 232613.2 722292.11 291152.806 663752.46
    ## 2 32327.45 0.5116918  51.16918      0.0 146447.56      0.000 126538.21
    ## 3 42375.97 0.4613532  46.13532      0.0 201004.76   8796.105 174906.87
    ## 4 77945.14 0.2454096  24.54096 116839.1 518385.81 164842.771 470382.10
    ## 5 22137.40 1.0000000 100.00000      0.0  79159.55      0.000  65525.90
    ## 6 14854.69 1.0000000 100.00000      0.0  53117.83      0.000  43969.34
    ##       CI68left CI68right
    ## 1 382926.74707 571978.52
    ## 2  31029.29567  95325.86
    ## 3  49710.36671 133992.61
    ## 4 240099.27894 395125.60
    ## 5    122.68804  44152.11
    ## 6     82.32642  29627.05

    ## Titles (list object) for estimate
    titlelst1.3 <- area1.3$titlelst
    names(titlelst1.3)

output

    ##  [1] "title.estpse"  "title.unitvar" "title.ref"     "outfn.estpse"
    ##  [5] "outfn.rawdat"  "outfn.param"   "title.rowvar"  "title.row"
    ##  [9] "title.colvar"  "title.col"     "title.unit"

    titlelst1.3

output

    ## $title.estpse
    ## [1] "Area, in acres (percent sampling error), by forest type and stand-size class on forest land"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] ""
    ##
    ## $outfn.estpse
    ## [1] "WY_area_FORTYPCD_STDSZCD_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "WY_area_FORTYPCD_STDSZCD_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Area, in acres (percent sampling error), by forest type on forest land"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Area, in acres (percent sampling error), by stand-size class on forest land"
    ##
    ## $title.unit
    ## [1] "acres"

    # List output files in outfolder
    list.files(outfolder, pattern = "WY_area")

output

    ## [1] "WY_area_FORTYPCD_STDSZCD_forestland.csv"

    list.files(paste0(outfolder, "/rawdata"), pattern = "WY_area")

output

    ## [1] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_colest.csv"
    ## [2] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_domdat.csv"
    ## [3] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_grpest.csv"
    ## [4] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_rowest.csv"
    ## [5] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_totest.csv"
    ## [6] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_colest.csv"
    ## [7] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_grpest.csv"
    ## [8] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_rowest.csv"
    ## [9] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_totest.csv"

#### POP2: 2.1 Area by forest type and stand-size class, Bighorn National Forest

View Example

We now shift to producing estimates for the Bighorn National Forest. In this example, we utilize the `title_opts` argument to customize the title outputs.

    area2.1 <- modGBarea(GBpopdat = GBpopdat.bh,       # pop - population calculations for Bighorn NF, post-stratification
                         landarea = "FOREST",          # est - forest land filter
                         rowvar = "FORTYPCD",          # est - row domain
                         colvar = "STDSZCD",           # est - column domain
                         returntitle = TRUE,           # out - return title information
                         title_opts = title_options(title.ref = "Bighorn National Forest, 2011-2013"),  # title - customize title reference
                         table_opts = table_options(row.FIAname = TRUE,         # table - return FIA row names
                                                    col.FIAname = TRUE))        # table - return FIA column names

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area2.1)

output

    ## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "states"   "invyr"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from our ouputted list:

    area2.1$est

output

    ##                        Forest type Large diameter Medium diameter
    ## 1                      Douglas-fir        19819.4              --
    ## 2                 Engelmann spruce        45091.6         19819.4
    ## 3 Engelmann spruce / subalpine fir        49548.6          9909.7
    ## 4                    Subalpine fir        24774.3              --
    ## 5                   Lodgepole pine       164008.2        203149.2
    ## 6                            Aspen             --              --
    ## 7                       Nonstocked             --              --
    ## 8                            Other             --              --
    ## 9                            Total       303242.1        232878.3
    ##   Small diameter Nonstocked    Total
    ## 1        19819.4         --  39638.9
    ## 2             --         --    64911
    ## 3         9909.7         --    69368
    ## 4        20317.3         --  45091.6
    ## 5        40136.7         -- 407294.1
    ## 6        19819.4         --  19819.4
    ## 7             --    19819.4  19819.4
    ## 8             --         --       --
    ## 9       110002.6    19819.4 665942.4

We can also look at raw data and titles for estimate, as shown below. Note the change in titles.

    raw2.1 <- area2.1$raw      # extract raw data list object from output
    names(raw2.1)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "unit_colest"
    ##  [6] "colest"      "unit_grpest" "grpest"      "domdat"      "domdatqry"
    ## [11] "module"      "esttype"     "popType"     "GBmethod"    "rowvar"
    ## [16] "colvar"      "areaunits"

    head(raw2.1$unit_grpest)  # estimates by row and group domains

output

    ##   ONEUNIT FORTYPCD STDSZCD        nhat      nhat.var NBRPLT.gt0
    ## 1       1      201       1 0.017816796 0.00032226595          1
    ## 2       1      201       3 0.017816796 0.00032226595          1
    ## 3       1      265       1 0.040535366 0.00067996313          3
    ## 4       1      265       2 0.017816796 0.00032226595          1
    ## 5       1      266       1 0.044541990 0.00069287179          3
    ## 6       1      266       2 0.008908398 0.00008056649          1
    ##   Stand-size class                      Forest type AREAUSED       est
    ## 1   Large diameter                      Douglas-fir  1112401 19819.430
    ## 2   Small diameter                      Douglas-fir  1112401 19819.430
    ## 3   Large diameter                 Engelmann spruce  1112401 45091.601
    ## 4  Medium diameter                 Engelmann spruce  1112401 19819.430
    ## 5   Large diameter Engelmann spruce / subalpine fir  1112401 49548.576
    ## 6  Medium diameter Engelmann spruce / subalpine fir  1112401  9909.715
    ##     est.var    est.se    est.cv       pse CI99left CI99right CI95left CI95right
    ## 1 398783824 19969.572 1.0075755 100.75755        0  71257.64        0  58959.07
    ## 2 398783824 19969.572 1.0075755 100.75755        0  71257.64        0  58959.07
    ## 3 841411576 29007.095 0.6432926  64.32926        0 119808.93        0 101944.46
    ## 4 398783824 19969.572 1.0075755 100.75755        0  71257.64        0  58959.07
    ## 5 857385222 29281.141 0.5909583  59.09583        0 124971.80        0 106938.56
    ## 6  99695956  9984.786 1.0075755 100.75755        0  35628.82        0  29479.54
    ##   CI68left CI68right
    ## 1     0.00  39678.33
    ## 2     0.00  39678.33
    ## 3 16245.27  73937.94
    ## 4     0.00  39678.33
    ## 5 20429.71  78667.44
    ## 6     0.00  19839.16

    # Titles (list object) for estimate
    titlelst2.1 <- area2.1$titlelst
    names(titlelst2.1)

output

    ##  [1] "title.est"     "title.pse"     "title.unitvar" "title.ref"
    ##  [5] "outfn.estpse"  "outfn.rawdat"  "outfn.param"   "title.rowvar"
    ##  [9] "title.row"     "title.colvar"  "title.col"     "title.unit"

    titlelst2.1

output

    ## $title.est
    ## [1] "Area, in acres, on forest land by forest type and stand-size class"
    ##
    ## $title.pse
    ## [1] "Percent sampling error of area, in acres, on forest land by forest type and stand-size class"
    ##
    ## $title.unitvar
    ## [1] "ONEUNIT"
    ##
    ## $title.ref
    ## [1] "Bighorn National Forest, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPCD_STDSZCD_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPCD_STDSZCD_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPCD_STDSZCD_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest type; Bighorn National Forest, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Area, in acres, on forest land by stand-size class; Bighorn National Forest, 2011-2013"
    ##
    ## $title.unit
    ## [1] "acres"

#### POP2: 2.2 Area by forest type group and primary disturbance class, Bighorn National Forest

View Example

Note: Since we only have one estimation unit within the population of interest, we set sumunits=FALSE. Let’s also add a few more table options to control the forest type groups displayed in the table (rowlut) and fill the NULL values with 0s (estnull).

    area2.2 <- modGBarea(GBpopdat = GBpopdat.bh,        # pop - population calculations for Bighorn NF, post-stratification
                         landarea = "FOREST",           # est - forest land filter
                         sumunits = TRUE,               # est - sum estimation units to population
                         rowvar = "FORTYPGRPCD",        # est - row domain
                         colvar = "DSTRBCD1",           # est - column domain
                         returntitle = TRUE,            # out - return title information
                         title_opts = title_options(
                            title.ref = "Bighorn National Forest, 2011-2013"  # title - customize title reference
                         ),
                        table_opts = table_options(
                          row.FIAname = TRUE,           # table - return FIA row names
                          col.FIAname = TRUE,           # table - return FIA column names
                          estnull = 0,
                          rowlut = c(180, 200, 220, 260, 280, 900, 999),
                          raw.keep0 = TRUE
                        ))

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area2.2)

output

    ## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "states"   "invyr"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from our ouputted list:

    area2.2$est

output

    ##                       Forest-type group No visible disturbance Disease
    ## 1                Pinyon / juniper group                      0       0
    ## 2                     Douglas-fir group                39638.9       0
    ## 3                  Ponderosa pine group                      0       0
    ## 4 Fir / spruce / mountain hemlock group               133781.2  4954.9
    ## 5                  Lodgepole pine group               352292.8 35181.9
    ## 6                 Aspen / birch / group                19819.4       0
    ## 7                            Nonstocked                19819.4       0
    ## 8                                 Total               565351.6 40136.7
    ##   Disease to trees    Fire    Wind    Total
    ## 1                0       0       0      0.0
    ## 2                0       0       0  39638.9
    ## 3                0       0       0      0.0
    ## 4          20317.3 20317.3       0 179370.6
    ## 5                0       0 19819.4 407294.1
    ## 6                0       0       0  19819.4
    ## 7                0       0       0  19819.4
    ## 8          20317.3 20317.3 19819.4 665942.4

We can also look at raw data and titles for estimate, as shown below. Note the change in titles.

    ## Raw data (list object) for estimate
    raw2.2 <- area2.2$raw      # extract raw data list object from output
    names(raw2.2)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "unit_colest"
    ##  [6] "colest"      "unit_grpest" "grpest"      "domdat"      "domdatqry"
    ## [11] "module"      "esttype"     "popType"     "GBmethod"    "rowvar"
    ## [16] "colvar"      "areaunits"

    head(raw2.2$unit_grpest)  # estimates by row and group domains

output

    ##   ONEUNIT FORTYPGRPCD DSTRBCD1       nhat     nhat.var NBRPLT.gt0
    ## 1       1         180        0 0.00000000 0.0000000000          0
    ## 2       1         180       20 0.00000000 0.0000000000          0
    ## 3       1         180       22 0.00000000 0.0000000000          0
    ## 4       1         180       30 0.00000000 0.0000000000          0
    ## 5       1         180       52 0.00000000 0.0000000000          0
    ## 6       1         200        0 0.03563359 0.0006284186          2
    ##        Forest-type group    Primary disturbance AREAUSED      est   est.var
    ## 1 Pinyon / juniper group No visible disturbance  1112401     0.00         0
    ## 2 Pinyon / juniper group                Disease  1112401     0.00         0
    ## 3 Pinyon / juniper group       Disease to trees  1112401     0.00         0
    ## 4 Pinyon / juniper group                   Fire  1112401     0.00         0
    ## 5 Pinyon / juniper group                   Wind  1112401     0.00         0
    ## 6      Douglas-fir group No visible disturbance  1112401 39638.86 777628457
    ##     est.se    est.cv      pse CI99left CI99right CI95left CI95right CI68left
    ## 1     0.00       NaN      NaN        0       0.0        0       0.0     0.00
    ## 2     0.00       NaN      NaN        0       0.0        0       0.0     0.00
    ## 3     0.00       NaN      NaN        0       0.0        0       0.0     0.00
    ## 4     0.00       NaN      NaN        0       0.0        0       0.0     0.00
    ## 5     0.00       NaN      NaN        0       0.0        0       0.0     0.00
    ## 6 27885.99 0.7035013 70.35013        0  111468.4        0   94294.4 11907.42
    ##   CI68right
    ## 1       0.0
    ## 2       0.0
    ## 3       0.0
    ## 4       0.0
    ## 5       0.0
    ## 6   67370.3

    ## Titles (list object) for estimate
    titlelst2.2 <- area2.2$titlelst
    names(titlelst2.2)

output

    ##  [1] "title.est"     "title.pse"     "title.unitvar" "title.ref"
    ##  [5] "outfn.estpse"  "outfn.rawdat"  "outfn.param"   "title.rowvar"
    ##  [9] "title.row"     "title.colvar"  "title.col"     "title.unit"

    titlelst2.2

output

    ## $title.est
    ## [1] "Area, in acres, on forest land by forest-type group and primary disturbance"
    ##
    ## $title.pse
    ## [1] "Percent sampling error of area, in acres, on forest land by forest-type group and primary disturbance"
    ##
    ## $title.unitvar
    ## [1] "ONEUNIT"
    ##
    ## $title.ref
    ## [1] "Bighorn National Forest, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPGRPCD_DSTRBCD1_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPGRPCD_DSTRBCD1_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPGRPCD_DSTRBCD1_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest-type group"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest-type group; Bighorn National Forest, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Primary disturbance"
    ##
    ## $title.col
    ## [1] "Area, in acres, on forest land by primary disturbance; Bighorn National Forest, 2011-2013"
    ##
    ## $title.unit
    ## [1] "acres"

#### POP3: 3.1 Area by forest type group and primary disturbance class, Bighorn National Forest Districts

View Example

In this example, we add a filter to remove instances where there is no visible disturbance from the table output. This filter does not change the population data set the estimates are derived from, it only changes the output.

    area3.1 <- modGBarea(GBpopdat = GBpopdat.bhdist,    # pop - population calculations for Bighorn NF, post-stratification
                         landarea = "FOREST",           # est - forest land filter
                         sumunits = TRUE,               # est - sum estimation units to population
                         pcfilter = "DSTRBCD1 > 0",     # est - condition filter for table output
                         rowvar = "FORTYPGRPCD",        # est - row domain
                         colvar = "DSTRBCD1",           # est - column domain
                         returntitle = TRUE,            # out - return title information
                         title_opts = title_options(
                           title.ref = "Bighorn National Forest, 2011-2013"  # title - customize title reference
                         ),
                         table_opts = table_options(
                           row.FIAname = TRUE,          # table - return FIA row names
                           col.FIAname = TRUE           # table - return FIA column names
                         ))

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area3.1)

output

    ## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "states"   "invyr"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from our ouputted list:

    area3.1$est

output

    ##                       Forest-type group No visible disturbance Disease
    ## 1                     Douglas-fir group                     --      --
    ## 2 Fir / spruce / mountain hemlock group                     --  6382.1
    ## 3                  Lodgepole pine group                     -- 39310.6
    ## 4                 Aspen / birch / group                     --      --
    ## 5                            Nonstocked                     --      --
    ## 6                                 Other                     --      --
    ## 7                                 Total                     -- 45692.6
    ##   Disease to trees    Fire    Wind   Total
    ## 1               --      --      --      --
    ## 2          20164.4 20164.4      -- 46710.9
    ## 3               --      -- 18375.5 57686.1
    ## 4               --      --      --      --
    ## 5               --      --      --      --
    ## 6               --      --      --      --
    ## 7          20164.4 20164.4 18375.5  104397

We can also look at raw data and titles for estimate, as shown below:

    raw3.1 <- area3.1$raw
    names(raw3.1)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "unit_colest"
    ##  [6] "colest"      "unit_grpest" "grpest"      "domdat"      "domdatqry"
    ## [11] "module"      "esttype"     "popType"     "GBmethod"    "rowvar"
    ## [16] "colvar"      "areaunits"

    head(raw3.1$unit_rowest)   # estimates by estimation unit for row domains

output

    ##                       DISTRICTNA FORTYPGRPCD       nhat    nhat.var NBRPLT.gt0
    ## 1 Medicine Wheel Ranger District         260 0.12814256 0.006624630          3
    ## 2 Medicine Wheel Ranger District         280 0.10784120 0.006431702          2
    ## 3         Tongue Ranger District         280 0.04440949 0.002133382          1
    ##                       Forest-type group AREAUSED      est   est.var   est.se
    ## 1 Fir / spruce / mountain hemlock group 364522.8 46710.89 880260198 29669.18
    ## 2                  Lodgepole pine group 364522.8 39310.58 854624495 29233.96
    ## 3                  Lodgepole pine group 413774.9 18375.53 365255577 19111.66
    ##      est.cv       pse CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1 0.6351663  63.51663        0 123133.63        0 104861.41 17206.14  76215.64
    ## 2 0.7436665  74.36665        0 114612.27        0  96608.09 10238.64  68382.52
    ## 3 1.0400602 104.00602        0  67603.91        0  55833.70     0.00  37381.27

    raw3.1$rowest              # estimates for population for row domains

output

    ##                       Forest-type group      est    est.var NBRPLT.gt0   est.se
    ## 1 Fir / spruce / mountain hemlock group 46710.89  880260198          3 29669.18
    ## 2                  Lodgepole pine group 57686.11 1219880072          3 34926.78
    ##      est.cv      pse CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1 0.6351663 63.51663        0  123133.6        0  104861.4 17206.14  76215.64
    ## 2 0.6054626 60.54626        0  147651.5        0  126141.3 22952.90  92419.32

    head(raw3.1$unit_colest)   # estimates by estimation unit for column domains

output

    ##                       DISTRICTNA DSTRBCD1       nhat    nhat.var NBRPLT.gt0
    ## 1 Medicine Wheel Ranger District       20 0.12534917 0.008479138          2
    ## 2 Medicine Wheel Ranger District       22 0.05531730 0.003799284          1
    ## 3 Medicine Wheel Ranger District       30 0.05531730 0.003799284          1
    ## 4         Tongue Ranger District       52 0.04440949 0.002133382          1
    ##   Primary disturbance AREAUSED      est    est.var   est.se    est.cv       pse
    ## 1             Disease 364522.8 45692.63 1126681476 33566.08 0.7346059  73.46059
    ## 2    Disease to trees 364522.8 20164.42  504836949 22468.58 1.1142686 111.42686
    ## 3                Fire 364522.8 20164.42  504836949 22468.58 1.1142686 111.42686
    ## 4                Wind 413774.9 18375.53  365255577 19111.66 1.0400602 104.00602
    ##   CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1        0 132153.12        0 111480.93 12312.58  79072.68
    ## 2        0  78039.64        0  64202.02     0.00  42508.47
    ## 3        0  78039.64        0  64202.02     0.00  42508.47
    ## 4        0  67603.91        0  55833.70     0.00  37381.27

    raw3.1$colest              # estimates for population for column domains

output

    ##   Primary disturbance      est    est.var NBRPLT.gt0   est.se    est.cv
    ## 1             Disease 45692.63 1126681476          2 33566.08 0.7346059
    ## 2    Disease to trees 20164.42  504836949          1 22468.58 1.1142686
    ## 3                Fire 20164.42  504836949          1 22468.58 1.1142686
    ## 4                Wind 18375.53  365255577          1 19111.66 1.0400602
    ##         pse CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1  73.46059        0 132153.12        0 111480.93 12312.58  79072.68
    ## 2 111.42686        0  78039.64        0  64202.02     0.00  42508.47
    ## 3 111.42686        0  78039.64        0  64202.02     0.00  42508.47
    ## 4 104.00602        0  67603.91        0  55833.70     0.00  37381.27

    ## Titles (list object) for estimate
    titlelst3.1 <- area3.1$titlelst
    names(titlelst3.1)

output

    ##  [1] "title.est"     "title.pse"     "title.unitvar" "title.ref"
    ##  [5] "outfn.estpse"  "outfn.rawdat"  "outfn.param"   "title.rowvar"
    ##  [9] "title.row"     "title.colvar"  "title.col"     "title.unit"

    titlelst3.1

output

    ## $title.est
    ## [1] "Area, in acres, on forest land by forest-type group and primary disturbance (DSTRBCD1 > 0)"
    ##
    ## $title.pse
    ## [1] "Percent sampling error of area, in acres, on forest land by forest-type group and primary disturbance (DSTRBCD1 > 0)"
    ##
    ## $title.unitvar
    ## [1] "DISTRICTNA"
    ##
    ## $title.ref
    ## [1] "Bighorn National Forest, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPGRPCD_DSTRBCD1_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPGRPCD_DSTRBCD1_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPGRPCD_DSTRBCD1_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest-type group"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest-type group (DSTRBCD1 > 0); Bighorn National Forest, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Primary disturbance"
    ##
    ## $title.col
    ## [1] "Area, in acres, on forest land by primary disturbance (DSTRBCD1 > 0); Bighorn National Forest, 2011-2013"
    ##
    ## $title.unit
    ## [1] "acres"

#### POP4: 4.1 Area by forest type group and stand-size class, Rhode Island, 2019

View Example

Note: estimates should match other FIA tools.

    area4.1 <- modGBarea(GBpopdat = GBpopdat.RI,        # pop - population calculations for Bighorn NF, post-stratification
                         landarea = "FOREST",           # est - forest land filter
                         sumunits = TRUE,               # est - sum estimation units to population
                         rowvar = "FORTYPCD",           # est - row domain
                         colvar = "STDSZCD",            # est - column domain
                         returntitle = TRUE,            # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,          # table - return FIA row names
                           col.FIAname = TRUE           # table - return FIA column names
                         ))

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area4.1)

output

    ## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "states"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from our ouputted list:

    area4.1$est

output

    ##                                          Forest type Large diameter
    ## 1                                 Eastern white pine        29664.4
    ## 2                                    Eastern hemlock         1015.5
    ## 3                                         Pitch pine        11079.8
    ## 4  Eastern white pine / northern red oak / white ash        11494.2
    ## 5                              Other pine / hardwood         1267.4
    ## 6                                       Chestnut oak             --
    ## 7                      White oak / red oak / hickory        75625.8
    ## 8                                          White oak        11354.2
    ## 9                                   Northern red oak        31766.7
    ## 10      Yellow-poplar / white oak / northern red oak         3745.7
    ## 11                                       Scarlet oak         6384.2
    ## 12            Chestnut oak / black oak / scarlet oak        31237.2
    ## 13                                   Red maple / oak        15559.7
    ## 14                            Mixed upland hardwoods         4800.7
    ## 15               Sweetbay / swamp tupelo / red maple        18424.4
    ## 16                       Silver maple / American elm         1411.5
    ## 17                               Red maple / lowland         3187.3
    ## 18                Sugar maple / beech / yellow birch           8744
    ## 19                                      Black cherry         2304.4
    ## 20                                Red maple / upland         3745.7
    ## 21                                             Aspen         1990.2
    ## 22                                   Other hardwoods         1942.8
    ## 23                                        Nonstocked             --
    ## 24                                             Other             --
    ## 25                                             Total       276745.7
    ##    Medium diameter Small diameter Nonstocked    Total
    ## 1               --             --         --  29664.4
    ## 2               --             --         --   1015.5
    ## 3               --             --         --  11079.8
    ## 4           2888.3             --         --  14382.6
    ## 5               --             --         --   1267.4
    ## 6           3745.7             --         --   3745.7
    ## 7          22315.7             --         --  97941.5
    ## 8            653.5             --         --  12007.7
    ## 9               --             --         --  31766.7
    ## 10              --             --         --   3745.7
    ## 11         19346.9             --         --  25731.1
    ## 12           337.8             --         --    31575
    ## 13            1569             --         --  17128.7
    ## 14              --             --         --   4800.7
    ## 15          7108.3             --         --  25532.8
    ## 16              --             --         --   1411.5
    ## 17         10300.1             --         --  13487.4
    ## 18              --             --         --     8744
    ## 19              --             --         --   2304.4
    ## 20          5086.7             --         --   8832.4
    ## 21              --           3000         --   4990.1
    ## 22          3745.7          936.4         --   6624.9
    ## 23              --             --     3347.5   3347.5
    ## 24              --             --         --       --
    ## 25         77097.8         3936.4     3347.5 361127.4

We can also look at raw data and titles for estimate, as shown below. Note the change in titles.

    raw4.1 <- area4.1$raw
    names(raw4.1)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "unit_colest"
    ##  [6] "colest"      "unit_grpest" "grpest"      "domdat"      "domdatqry"
    ## [11] "module"      "esttype"     "popType"     "GBmethod"    "rowvar"
    ## [16] "colvar"      "areaunits"

    head(raw4.1$unit_grpest)  # estimates by row and group domains

output

    ##   ESTN_UNIT FORTYPCD STDSZCD        nhat       nhat.var NBRPLT.gt0
    ## 1         2      103       1 0.026662405 0.000160096639          4
    ## 2         2      105       1 0.001786383 0.000002880391          1
    ## 3         2      167       1 0.006389858 0.000037959142          1
    ## 4         2      401       1 0.014878180 0.000070343732          4
    ## 5         2      401       2 0.005081007 0.000012971659          2
    ## 6         2      409       1 0.002229531 0.000002996697          2
    ##   Stand-size class                                       Forest type AREAUSED
    ## 1   Large diameter                                Eastern white pine 568453.6
    ## 2   Large diameter                                   Eastern hemlock 568453.6
    ## 3   Large diameter                                        Pitch pine 568453.6
    ## 4   Large diameter Eastern white pine / northern red oak / white ash 568453.6
    ## 5  Medium diameter Eastern white pine / northern red oak / white ash 568453.6
    ## 6   Large diameter                             Other pine / hardwood 568453.6
    ##         est    est.var    est.se    est.cv      pse CI99left CI99right CI95left
    ## 1 15156.340 51733547.1 7192.6036 0.4745607 47.45607        0 33683.259 1059.096
    ## 2  1015.476   930768.0  964.7632 0.9500603 95.00603        0  3500.541    0.000
    ## 3  3632.338 12266097.9 3502.2989 0.9641997 96.41997        0 12653.662    0.000
    ## 4  8457.555 22730838.2 4767.6869 0.5637193 56.37193        0 20738.302    0.000
    ## 5  2888.317  4191655.5 2047.3533 0.7088396 70.88396        0  8161.949    0.000
    ## 6  1267.385   968351.2  984.0484 0.7764399 77.64399        0  3802.126    0.000
    ##   CI95right   CI68left CI68right
    ## 1 29253.584 8003.59845 22309.081
    ## 2  2906.377   56.05943  1974.892
    ## 3 10496.717  149.44889  7115.226
    ## 4 17802.049 3716.29112 13198.819
    ## 5  6901.056  852.31018  4924.323
    ## 6  3196.085  288.79044  2245.980

    ## Titles (list object) for estimate
    titlelst4.1 <- area4.1$titlelst
    names(titlelst4.1)

output

    ##  [1] "title.est"     "title.pse"     "title.unitvar" "title.ref"
    ##  [5] "outfn.estpse"  "outfn.rawdat"  "outfn.param"   "title.rowvar"
    ##  [9] "title.row"     "title.colvar"  "title.col"     "title.unit"

    titlelst4.1

output

    ## $title.est
    ## [1] "Area, in acres, on forest land by forest type and stand-size class"
    ##
    ## $title.pse
    ## [1] "Percent sampling error of area, in acres, on forest land by forest type and stand-size class"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] ""
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPCD_STDSZCD_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPCD_STDSZCD_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPCD_STDSZCD_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest type"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Area, in acres, on forest land by stand-size class"
    ##
    ## $title.unit
    ## [1] "acres"

### modGBtree

`FIESTA`‘s `modGBtree` function generates tree estimates based Scott et al. 2005 (’the green-book’) for mapped forest inventory plots. The non-ratio estimator for estimating tree attributes by stratum and domain is used. Plots that are totally nonsampled are excluded from estimation dataset. Next, an adjustment factor is calculated by strata to adjust for nonsampled (nonresponse) conditions that have proportion less than 1. Attributes adjusted to a per-acre value are summed by plot, divided by the adjustment factor, and averaged by stratum. Strata means are combined using the strata weights and then expanded to using the total land area in the population.

If there are more than one estimation unit (i.e., subpopulation) within the population, estimates are generated by estimation unit. If `sumunits = TRUE`, the estimates and percent standard errors returned are a sum combination of all estimation units. If `rawdata = TRUE`, the raw data returned will include estimates by estimation unit.

Parameters defined in the following examples are organized by category: population data (pop); estimation information (est); and output details (out).

The following reference table can be used for defining `estvar` and `estvar.filter`:

    FIESTAutils::ref_estvar[, c("ESTTITLE", "ESTVAR", "ESTFILTER", "ESTUNITS")]

output

    ##                                                                                                                                                             ESTTITLE
    ## 1                                                                                                               Number of trees (timber species at least 1 inch dia)
    ## 2                                                                                                                              Number of trees (at least 1 inch dia)
    ## 3                                                                                                             Number of trees (woodland species at least 1 inch dia)
    ## 4                                                                                                          Number of live trees (timber species at least 1 inch dia)
    ## 5                                                                                                                         Number of live trees (at least 1 inch dia)
    ## 6                                                                                                        Number of live trees (woodland species at least 1 inch dia)
    ## 7                                                                                                          Number of live trees (timber species at least 5 inch dia)
    ## 8                                                                                                                         Number of live trees (at least 5 inch dia)
    ## 9                                                                                                        Number of live trees (woodland species at least 5 inch dia)
    ## 10                                                                                                Number of standing-dead trees (timber species at least 5 inch dia)
    ## 11                                                                                                               Number of standing-dead trees (at least 5 inch dia)
    ## 12                                                                                              Number of standing-dead trees (woodland species at least 5 inch dia)
    ## 13                                                                                                               Number of growing-stock trees (at least 5 inch dia)
    ## 14                                                                                                               Number of saplings (timber species 1 to 5 inch dia)
    ## 15                                                                                                                              Number of saplings (1 to 5 inch dia)
    ## 16                                                                                                             Number of saplings (woodland species 1 to 5 inch dia)
    ## 17                                                                                                          Basal area of trees (timber species at least 1 inch dia)
    ## 18                                                                                                                         Basal area of trees (at least 1 inch dia)
    ## 19                                                                                                        Basal area of trees (woodland species at least 1 inch dia)
    ## 20                                                                                                     Basal area of live trees (timber species at least 1 inch dia)
    ## 21                                                                                                                    Basal area of live trees (at least 1 inch dia)
    ## 22                                                                                                   Basal area of live trees (woodland species at least 1 inch dia)
    ## 23                                                                                                     Basal area of live trees (timber species at least 5 inch dia)
    ## 24                                                                                                                    Basal area of live trees (at least 5 inch dia)
    ## 25                                                                                                   Basal area of live trees (woodland species at least 5 inch dia)
    ## 26                                                                                                     Basal area of dead trees (timber species at least 5 inch dia)
    ## 27                                                                                                                    Basal area of dead trees (at least 5 inch dia)
    ## 28                                                                                                   Basal area of dead trees (woodland species at least 5 inch dia)
    ## 29                                                                                                           Basal area of saplings (timber species 1 to 5 inch dia)
    ## 30                                                                                                                          Basal area of saplings (1 to 5 inch dia)
    ## 31                                                                                                         Basal area of saplings (woodland species 1 to 5 inch dia)
    ## 32                                                                                   Net merchantable bole wood volume of trees (timber species at least 5 inch dia)
    ## 33                                                                             Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia)
    ## 34                                                                    Net merchantable bole  wood volume of standing-dead trees (timber species at least 5 inch dia)
    ## 35                                                                                    Net merchantable bole wood volume of growing-stock trees (at least 5 inch dia)
    ## 36                                                                                 Gross merchantable bole wood volume of trees (timber species at least 5 inch dia)
    ## 37                                                                            Gross merchantable bole wood volume of live trees (timber species at least 5 inch dia)
    ## 38                                                                   Gross merchantable bole wood volume of standing-dead trees (timber species at least 5 inch dia)
    ## 39                                                                                  Gross merchantable bole wood volume of growing-stock trees (at least 5 inch dia)
    ## 40                                                              Gross stem-top (above 4-inch top dia) wood volume of live trees (timber species at least 5 inch dia)
    ## 41                                                                                              Sound bole wood volume of trees (timber species at least 5 inch dia)
    ## 42                                                                                         Sound bole wood volume of live trees (timber species at least 5 inch dia)
    ## 43                                                                                Sound bole wood volume of standing-dead trees (timber species at least 5 inch dia)
    ## 44                                                                                                                         Net sawlog wood volume of sawtimber trees
    ## 45                                                                                                                       Gross sawlog wood volume of sawtimber trees
    ## 46                                                                                                                         Net sawlog wood volume of sawtimber trees
    ## 47                                                                                                                       Gross sawlog wood volume of sawtimber trees
    ## 48                                                                                                                         Net sawlog wood volume of sawtimber trees
    ## 49                                                                                                                       Gross sawlog wood volume of sawtimber trees
    ## 50                                                                                                 Gross total-stem wood volume (timber species at least 1 inch dia)
    ## 51                                                      Gross total-stem wood volume (timber species at least 1 inch dia and woodland species at least 1.5 inch dia)
    ## 52                                                                                             Gross total-stem wood volume (woodland species at least 1.5 inch dia)
    ## 53                                                                                   Gross total-stem wood volume of live trees (timber species at least 1 inch dia)
    ## 54                                        Gross total-stem wood volume of live trees (timber species at least 1 inch dia and woodland species at least 1.5 inch dia)
    ## 55                                                                               Gross total-stem wood volume of live trees (woodland species at least 1.5 inch dia)
    ## 56                                                                                   Gross total-stem wood volume of live trees (timber species at least 5 inch dia)
    ## 57                                                                                                  Gross total-stem wood volume of live trees (at least 5 inch dia)
    ## 58                                                                                 Gross total-stem wood volume of live trees (woodland species at least 5 inch dia)
    ## 59                                                                          Gross total-stem wood volume of standing-dead trees (timber species at least 5 inch dia)
    ## 60                                                                                         Gross total-stem wood volume of standing-dead trees (at least 5 inch dia)
    ## 61                                                                        Gross total-stem wood volume of standing-dead trees (woodland species at least 5 inch dia)
    ## 62                                                                Gross total-stem wood volume of live saplings (timber species at least 1 and less than 5 inch dia)
    ## 63     Gross total-stem wood volume of live saplings (timber species at least 1 and less than 5 inch dia and woodland species at least 1.5 and less than 5 inch dia)
    ## 64                                                        Gross total-stem wood volume of live saplings (woodland species at least 1.5 dia and less than 5 inch dia)
    ## 65                                                                                           Gross sound total-stem wood volume (timber species at least 1 inch dia)
    ## 66                                                Gross sound total-stem wood volume (timber species at least 1 inch dia and woodland species at least 1.5 inch dia)
    ## 67                                                                                       Gross sound total-stem wood volume (woodland species at least 1.5 inch dia)
    ## 68                                                                             Gross sound total-stem wood volume of live trees (timber species at least 1 inch dia)
    ## 69                                  Gross sound total-stem wood volume of live trees (timber species at least 1 inch dia and woodland species at least 1.5 inch dia)
    ## 70                                                                         Gross sound total-stem wood volume of live trees (woodland species at least 1.5 inch dia)
    ## 71                                                                             Gross sound total-stem wood volume of live trees (timber species at least 5 inch dia)
    ## 72                                                                                            Gross sound total-stem wood volume of live trees (at least 5 inch dia)
    ## 73                                                                           Gross sound total-stem wood volume of live trees (woodland species at least 5 inch dia)
    ## 74                                                                    Gross sound total-stem wood volume of standing-dead trees (timber species at least 5 inch dia)
    ## 75                                                                                   Gross sound total-stem wood volume of standing-dead trees (at least 5 inch dia)
    ## 76                                                                  Gross sound total-stem wood volume of standing-dead trees (woodland species at least 5 inch dia)
    ## 77                                                          Gross sound total-stem wood volume of live saplings (timber species at least 1 and less than 5 inch dia)
    ## 78  Gross sound total-stem wood volume of live trees (timber species at least 1 and less than 5 inch dia and woodland species at least 1.5 and less than 5 inch dia)
    ## 79                                                     Gross sound total-stem wood volume of live trees (woodland species at least 1.5 dia and less than 5 inch dia)
    ## 80                                                                                   Gross total-stem bark volume of live trees (timber species at least 1 inch dia)
    ## 81                                        Gross total-stem bark volume of live trees (timber species at least 1 inch dia and woodland species at least 1.5 inch dia)
    ## 82                                                                               Gross total-stem bark volume of live trees (woodland species at least 1.5 inch dia)
    ## 83                                                                          Gross total-stem bark volume of standing-dead trees (timber species at least 1 inch dia)
    ## 84                               Gross total-stem bark volume of standing-dead trees (timber species at least 1 inch dia and woodland species at least 1.5 inch dia)
    ## 85                                                                      Gross total-stem bark volume of standing-dead trees (woodland species at least 1.5 inch dia)
    ## 86                                                                Gross total-stem bark volume of live saplings (timber species at least 1 and less than 5 inch dia)
    ## 87                                                                                              Aboveground dry weight of trees (timber species at least 1 inch dia)
    ## 88                                                                                                             Aboveground dry weight of trees (at least 1 inch dia)
    ## 89                                                                                            Aboveground dry weight of trees (woodland species at least 1 inch dia)
    ## 90                                                                                         Aboveground dry weight of live trees (timber species at least 1 inch dia)
    ## 91                                                                                                        Aboveground dry weight of live trees (at least 1 inch dia)
    ## 92                                                                                       Aboveground dry weight of live trees (woodland species at least 1 inch dia)
    ## 93                                                                                         Aboveground dry weight of live trees (timber species at least 5 inch dia)
    ## 94                                                                                                        Aboveground dry weight of live trees (at least 5 inch dia)
    ## 95                                                                                       Aboveground dry weight of live trees (woodland species at least 5 inch dia)
    ## 96                                                                                Aboveground dry weight of standing-dead trees (timber species at least 1 inch dia)
    ## 97                                                                                               Aboveground dry weight of standing-dead trees (at least 1 inch dia)
    ## 98                                                                              Aboveground dry weight of standing-dead trees (woodland species at least 1 inch dia)
    ## 99                                                                                Aboveground dry weight of standing-dead trees (timber species at least 5 inch dia)
    ## 100                                                                                              Aboveground dry weight of standing-dead trees (at least 5 inch dia)
    ## 101                                                                             Aboveground dry weight of standing-dead trees (woodland species at least 5 inch dia)
    ## 102                                                                     Aboveground dry weight of live saplings (timber species at least 1 and less than 5 inch dia)
    ## 103                                                                                    Aboveground dry weight of live saplings (at least 1 and less than 5 inch dia)
    ## 104                                                                   Aboveground dry weight of live saplings (woodland species at least 1 and less than 5 inch dia)
    ## 105                                                                                             Belowground dry weight of trees (timber species at least 1 inch dia)
    ## 106                                                                                                            Belowground dry weight of trees (at least 1 inch dia)
    ## 107                                                                                           Belowground dry weight of trees (woodland species at least 1 inch dia)
    ## 108                                                                                        Belowground dry weight of live trees (timber species at least 1 inch dia)
    ## 109                                                                                                       Belowground dry weight of live trees (at least 1 inch dia)
    ## 110                                                                                      Belowground dry weight of live trees (woodland species at least 1 inch dia)
    ## 111                                                                                        Belowground dry weight of live trees (timber species at least 5 inch dia)
    ## 112                                                                                                       Belowground dry weight of live trees (at least 5 inch dia)
    ## 113                                                                                      Belowground dry weight of live trees (woodland species at least 5 inch dia)
    ## 114                                                                               Belowground dry weight of standing-dead trees (timber species at least 5 inch dia)
    ## 115                                                                                              Belowground dry weight of standing-dead trees (at least 5 inch dia)
    ## 116                                                                             Belowground dry weight of standing-dead trees (woodland species at least 5 inch dia)
    ## 117                                                                     Belowground dry weight of live saplings (timber species at least 1 and less than 5 inch dia)
    ## 118                                                                                    Belowground dry weight of live saplings (at least 1 and less than 5 inch dia)
    ## 119                                                                   Belowground dry weight of live saplings (woodland species at least 1 and less than 5 inch dia)
    ## 120                                                                        Oven-dry biomass of wood in the merchantable bole of timber species (at least 5 inch dia)
    ## 121                                                          Oven-dry biomass of wood in the merchantable bole of standing-dead timber species (at least 5 inch dia)
    ## 122                                                                                    Oven-dry biomass of wood in the stump of timber species (at least 5 inch dia)
    ## 123                                                                      Oven-dry biomass of wood in the stump of standing-dead timber species (at least 5 inch dia)
    ## 124                                                                               Oven-dry biomass of wood in the total stem of timber species (at least 1 inch dia)
    ## 125                                                                          Oven-dry biomass of wood in the total stem of live timber species (at least 1 inch dia)
    ## 126                                                                          Oven-dry biomass of wood in the total stem of live timber species (at least 5 inch dia)
    ## 127                                                                 Oven-dry biomass of wood in the total stem of standing-dead timber species (at least 5 inch dia)
    ## 128                                                                                   Oven-dry biomass in the branches/limbs of timber species (at least 1 inch dia)
    ## 129                                                                              Oven-dry biomass in the branches/limbs of live timber species (at least 1 inch dia)
    ## 130                                                                              Oven-dry biomass in the branches/limbs of live timber species (at least 5 inch dia)
    ## 131                                                                     Oven-dry biomass in the branches/limbs of standing-dead timber species (at least 5 inch dia)
    ## 132                                                                                   Oven-dry biomass of foliage in live trees (timber species at least 1 inch dia)
    ## 133                                                                                                  Oven-dry biomass of foliage in live trees (at least 1 inch dia)
    ## 134                                                                                 Oven-dry biomass of foliage in live trees (woodland species at least 1 inch dia)
    ## 135                                                                                   Oven-dry biomass of foliage in live trees (timber species at least 5 inch dia)
    ## 136                                                                                                  Oven-dry biomass of foliage in live trees (at least 5 inch dia)
    ## 137                                                                                 Oven-dry biomass of foliage in live trees (woodland species at least 5 inch dia)
    ## 138                                                                Oven-dry biomass of foliage in live saplings (timber species at least 1 and less than 5 inch dia)
    ## 139                                                                               Oven-dry biomass of foliage in live saplings (at least 1 and less than 5 inch dia)
    ## 140                                                              Oven-dry biomass of foliage in live saplings (woodland species at least 1 and less than 5 inch dia)
    ## 141                                                                                                 Aboveground carbon in trees (timber species at least 1 inch dia)
    ## 142                                                                                                                Aboveground carbon in trees (at least 1 inch dia)
    ## 143                                                                                               Aboveground carbon in trees (woodland species at least 1 inch dia)
    ## 144                                                                                            Aboveground carbon in live trees (timber species at least 1 inch dia)
    ## 145                                                                                                           Aboveground carbon in live trees (at least 1 inch dia)
    ## 146                                                                                          Aboveground carbon in live trees (woodland species at least 1 inch dia)
    ## 147                                                                                            Aboveground carbon in live trees (timber species at least 5 inch dia)
    ## 148                                                                                                           Aboveground carbon in live trees (at least 5 inch dia)
    ## 149                                                                                          Aboveground carbon in live trees (woodland species at least 5 inch dia)
    ## 150                                                                                   Aboveground carbon in standing-dead trees (timber species at least 5 inch dia)
    ## 151                                                                                                  Aboveground carbon in standing-dead trees (at least 5 inch dia)
    ## 152                                                                                 Aboveground carbon in standing-dead trees (woodland species at least 5 inch dia)
    ## 153                                                                         Aboveground carbon of live saplings (timber species at least 1 and less than 5 inch dia)
    ## 154                                                                                        Aboveground carbon of live saplings (at least 1 and less than 5 inch dia)
    ## 155                                                                       Aboveground carbon of live saplings (woodland species at least 1 and less than 5 inch dia)
    ## 156                                                                                                 Belowground carbon in trees (timber species at least 1 inch dia)
    ## 157                                                                                                                Belowground carbon in trees (at least 1 inch dia)
    ## 158                                                                                               Belowground carbon in trees (woodland species at least 1 inch dia)
    ## 159                                                                                            Belowground carbon in live trees (timber species at least 1 inch dia)
    ## 160                                                                                                           Belowground carbon in live trees (at least 1 inch dia)
    ## 161                                                                                          Belowground carbon in live trees (woodland species at least 1 inch dia)
    ## 162                                                                                            Belowground carbon in live trees (timber species at least 5 inch dia)
    ## 163                                                                                                           Belowground carbon in live trees (at least 5 inch dia)
    ## 164                                                                                          Belowground carbon in live trees (woodland species at least 5 inch dia)
    ## 165                                                                                   Belowground carbon in standing-dead trees (timber species at least 5 inch dia)
    ## 166                                                                                                  Belowground carbon in standing-dead trees (at least 5 inch dia)
    ## 167                                                                                 Belowground carbon in standing-dead trees (woodland species at least 5 inch dia)
    ## 168                                                                         Belowground carbon of live saplings (timber species at least 1 and less than 5 inch dia)
    ## 169                                                                                        Belowground carbon of live saplings (at least 1 and less than 5 inch dia)
    ## 170                                                                       Belowground carbon of live saplings (woodland species at least 1 and less than 5 inch dia)
    ## 171                                                                                                                                                         Tree age
    ## 172                                                                                                        Number of seedlings (timber species less than 1 inch dia)
    ## 173                                                                                                                       Number of seedlings (less than 1 inch dia)
    ## 174                                                                                                      Number of seedlings (woodland species less than 1 inch dia)
    ## 175                                                                                                             Number of trees (timber species including seedlings)
    ## 176                                                                                                                            Number of trees (including seedlings)
    ## 177                                                                                                           Number of trees (woodland species including seedlings)
    ## 178                                                                                                                                                    Percent cover
    ##             ESTVAR                                          ESTFILTER
    ## 1        TPA_UNADJ
    ## 2        TPA_UNADJ
    ## 3        TPA_UNADJ
    ## 4        TPA_UNADJ                         STATUSCD == 1 & DIA >= 1.0
    ## 5        TPA_UNADJ                         STATUSCD == 1 & DIA >= 1.0
    ## 6        TPA_UNADJ                         STATUSCD == 1 & DIA >= 1.0
    ## 7        TPA_UNADJ                         STATUSCD == 1 & DIA >= 5.0
    ## 8        TPA_UNADJ                         STATUSCD == 1 & DIA >= 5.0
    ## 9        TPA_UNADJ                         STATUSCD == 1 & DIA >= 5.0
    ## 10       TPA_UNADJ STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 11       TPA_UNADJ STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 12       TPA_UNADJ STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 13       TPA_UNADJ                         TREECLCD == 2 & DIA >= 5.0
    ## 14       TPA_UNADJ                                         DIA <  5.0
    ## 15       TPA_UNADJ                                         DIA <  5.0
    ## 16       TPA_UNADJ                                         DIA <  5.0
    ## 17              BA
    ## 18              BA
    ## 19              BA
    ## 20              BA                         STATUSCD == 1 & DIA >= 1.0
    ## 21              BA                         STATUSCD == 1 & DIA >= 1.0
    ## 22              BA                         STATUSCD == 1 & DIA >= 1.0
    ## 23              BA                         STATUSCD == 1 & DIA >= 5.0
    ## 24              BA                         STATUSCD == 1 & DIA >= 5.0
    ## 25              BA                         STATUSCD == 1 & DIA >= 5.0
    ## 26              BA STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 27              BA STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 28              BA STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 29              BA                                         DIA <  5.0
    ## 30              BA                                         DIA <  5.0
    ## 31              BA                                         DIA <  5.0
    ## 32        VOLCFNET                                         DIA >= 5.0
    ## 33        VOLCFNET                         STATUSCD == 1 & DIA >= 5.0
    ## 34        VOLCFNET STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 35        VOLCFNET                         TREECLCD == 2 & DIA >= 5.0
    ## 36        VOLCFGRS                                         DIA >= 5.0
    ## 37        VOLCFGRS                         STATUSCD == 1 & DIA >= 5.0
    ## 38        VOLCFGRS STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 39        VOLCFGRS                         TREECLCD == 2 & DIA >= 5.0
    ## 40    VOLCFGRS_TOP                                         DIA >= 5.0
    ## 41        VOLCFSND                                         DIA >= 5.0
    ## 42        VOLCFSND                         STATUSCD == 1 & DIA >= 5.0
    ## 43        VOLCFSND STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 44        VOLCSNET                                      STATUSCD == 1
    ## 45        VOLCSGRS                                      STATUSCD == 1
    ## 46        VOLBFNET                      TREECLCD == 2 & STATUSCD == 1
    ## 47        VOLBFGRS                      TREECLCD == 2 & STATUSCD == 1
    ## 48        VOLBSNET                      TREECLCD == 2 & STATUSCD == 1
    ## 49        VOLBSGRS                      TREECLCD == 2 & STATUSCD == 1
    ## 50        VOLTSGRS
    ## 51        VOLTSGRS
    ## 52        VOLTSGRS
    ## 53        VOLTSGRS                                      STATUSCD == 1
    ## 54        VOLTSGRS                                      STATUSCD == 1
    ## 55        VOLTSGRS                                      STATUSCD == 1
    ## 56        VOLTSGRS                         STATUSCD == 1 & DIA >= 5.0
    ## 57        VOLTSGRS                         STATUSCD == 1 & DIA >= 5.0
    ## 58        VOLTSGRS                         STATUSCD == 1 & DIA >= 5.0
    ## 59        VOLTSGRS STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 60        VOLTSGRS STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 61        VOLTSGRS STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 62        VOLTSGRS                                            DIA < 5
    ## 63        VOLTSGRS                                            DIA < 5
    ## 64        VOLTSGRS                                            DIA < 5
    ## 65        VOLTSSND
    ## 66        VOLTSSND
    ## 67        VOLTSSND
    ## 68        VOLTSSND                                      STATUSCD == 1
    ## 69        VOLTSSND                                      STATUSCD == 1
    ## 70        VOLTSSND                                      STATUSCD == 1
    ## 71        VOLTSSND                         STATUSCD == 1 & DIA >= 5.0
    ## 72        VOLTSSND                         STATUSCD == 1 & DIA >= 5.0
    ## 73        VOLTSSND                         STATUSCD == 1 & DIA >= 5.0
    ## 74        VOLTSSND STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 75        VOLTSSND STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 76        VOLTSSND STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 77        VOLTSSND                                            DIA < 5
    ## 78        VOLTSSND                                            DIA < 5
    ## 79        VOLTSSND                                            DIA < 5
    ## 80   VOLTSGRS_BARK                         STATUSCD == 1 & DIA >= 1.0
    ## 81   VOLTSGRS_BARK                         STATUSCD == 1 & DIA >= 1.0
    ## 82   VOLTSGRS_BARK                         STATUSCD == 1 & DIA >= 1.0
    ## 83   VOLTSGRS_BARK STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 1.0
    ## 84   VOLTSGRS_BARK STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 1.0
    ## 85   VOLTSGRS_BARK STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 1.0
    ## 86   VOLTSGRS_BARK                                            DIA < 5
    ## 87       DRYBIO_AG                                         DIA >= 1.0
    ## 88       DRYBIO_AG                                         DIA >= 1.0
    ## 89       DRYBIO_AG                                         DIA >= 1.0
    ## 90       DRYBIO_AG                         STATUSCD == 1 & DIA >= 1.0
    ## 91       DRYBIO_AG                         STATUSCD == 1 & DIA >= 1.0
    ## 92       DRYBIO_AG                         STATUSCD == 1 & DIA >= 1.0
    ## 93       DRYBIO_AG                         STATUSCD == 1 & DIA >= 5.0
    ## 94       DRYBIO_AG                         STATUSCD == 1 & DIA >= 5.0
    ## 95       DRYBIO_AG                         STATUSCD == 1 & DIA >= 5.0
    ## 96       DRYBIO_AG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 1.0
    ## 97       DRYBIO_AG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 1.0
    ## 98       DRYBIO_AG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 1.0
    ## 99       DRYBIO_AG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 100      DRYBIO_AG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 101      DRYBIO_AG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 102      DRYBIO_AG                         STATUSCD == 1 & DIA <  5.0
    ## 103      DRYBIO_AG                         STATUSCD == 1 & DIA <  5.0
    ## 104      DRYBIO_AG                         STATUSCD == 1 & DIA <  5.0
    ## 105      DRYBIO_BG                                         DIA >= 1.0
    ## 106      DRYBIO_BG                                         DIA >= 1.0
    ## 107      DRYBIO_BG                                         DIA >= 1.0
    ## 108      DRYBIO_BG                                      STATUSCD == 1
    ## 109      DRYBIO_BG                                      STATUSCD == 1
    ## 110      DRYBIO_BG                                      STATUSCD == 1
    ## 111      DRYBIO_BG                         STATUSCD == 1 & DIA >= 5.0
    ## 112      DRYBIO_BG                         STATUSCD == 1 & DIA >= 5.0
    ## 113      DRYBIO_BG                         STATUSCD == 1 & DIA >= 5.0
    ## 114      DRYBIO_BG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 115      DRYBIO_BG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 116      DRYBIO_BG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 117      DRYBIO_BG                                         DIA <  5.0
    ## 118      DRYBIO_BG                                         DIA <  5.0
    ## 119      DRYBIO_BG                                         DIA <  5.0
    ## 120    DRYBIO_BOLE                         STATUSCD == 1 & DIA >= 5.0
    ## 121    DRYBIO_BOLE STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 122   DRYBIO_STUMP                         STATUSCD == 1 & DIA >= 5.0
    ## 123   DRYBIO_STUMP STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 124    DRYBIO_STEM                                         DIA >= 1.0
    ## 125    DRYBIO_STEM                                      STATUSCD == 1
    ## 126    DRYBIO_STEM                         STATUSCD == 1 & DIA >= 5.0
    ## 127    DRYBIO_STEM STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 128  DRYBIO_BRANCH                                         DIA >= 1.0
    ## 129  DRYBIO_BRANCH                                      STATUSCD == 1
    ## 130  DRYBIO_BRANCH                         STATUSCD == 1 & DIA >= 5.0
    ## 131  DRYBIO_BRANCH STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 132 DRYBIO_FOLIAGE                                      STATUSCD == 1
    ## 133 DRYBIO_FOLIAGE                                      STATUSCD == 1
    ## 134 DRYBIO_FOLIAGE                                      STATUSCD == 1
    ## 135 DRYBIO_FOLIAGE                         STATUSCD == 1 & DIA >= 5.0
    ## 136 DRYBIO_FOLIAGE                         STATUSCD == 1 & DIA >= 5.0
    ## 137 DRYBIO_FOLIAGE                         STATUSCD == 1 & DIA >= 5.0
    ## 138 DRYBIO_FOLIAGE                                         DIA <  5.0
    ## 139 DRYBIO_FOLIAGE                                         DIA <  5.0
    ## 140 DRYBIO_FOLIAGE                                         DIA <  5.0
    ## 141      CARBON_AG                                         DIA >= 1.0
    ## 142      CARBON_AG                                         DIA >= 1.0
    ## 143      CARBON_AG                                         DIA >= 1.0
    ## 144      CARBON_AG                                      STATUSCD == 1
    ## 145      CARBON_AG                                      STATUSCD == 1
    ## 146      CARBON_AG                                      STATUSCD == 1
    ## 147      CARBON_AG                         STATUSCD == 1 & DIA >= 5.0
    ## 148      CARBON_AG                         STATUSCD == 1 & DIA >= 5.0
    ## 149      CARBON_AG                         STATUSCD == 1 & DIA >= 5.0
    ## 150      CARBON_AG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 151      CARBON_AG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 152      CARBON_AG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 153      CARBON_AG                                         DIA <  5.0
    ## 154      CARBON_AG                                         DIA <  5.0
    ## 155      CARBON_AG                                         DIA <  5.0
    ## 156      CARBON_BG                                         DIA >= 1.0
    ## 157      CARBON_BG                                         DIA >= 1.0
    ## 158      CARBON_BG                                         DIA >= 1.0
    ## 159      CARBON_BG                                      STATUSCD == 1
    ## 160      CARBON_BG                                      STATUSCD == 1
    ## 161      CARBON_BG                                      STATUSCD == 1
    ## 162      CARBON_BG                         STATUSCD == 1 & DIA >= 5.0
    ## 163      CARBON_BG                         STATUSCD == 1 & DIA >= 5.0
    ## 164      CARBON_BG                         STATUSCD == 1 & DIA >= 5.0
    ## 165      CARBON_BG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 166      CARBON_BG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 167      CARBON_BG STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 168      CARBON_BG                                         DIA <  5.0
    ## 169      CARBON_BG                                         DIA <  5.0
    ## 170      CARBON_BG                                         DIA <  5.0
    ## 171        TREEAGE
    ## 172      TPA_UNADJ
    ## 173      TPA_UNADJ
    ## 174      TPA_UNADJ
    ## 175      TPA_UNADJ                                      STATUSCD == 1
    ## 176      TPA_UNADJ                                      STATUSCD == 1
    ## 177      TPA_UNADJ                                      STATUSCD == 1
    ## 178  COVER_PCT_SUM
    ##                                     ESTUNITS
    ## 1                                      trees
    ## 2                                      trees
    ## 3                                      trees
    ## 4                                      trees
    ## 5                                      trees
    ## 6                                      trees
    ## 7                                      trees
    ## 8                                      trees
    ## 9                                      trees
    ## 10                                     trees
    ## 11                                     trees
    ## 12                                     trees
    ## 13                                     trees
    ## 14                                     trees
    ## 15                                     trees
    ## 16                                     trees
    ## 17                               square feet
    ## 18                               square feet
    ## 19                               square feet
    ## 20                               square feet
    ## 21                               square feet
    ## 22                               square feet
    ## 23                               square feet
    ## 24                               square feet
    ## 25                               square feet
    ## 26                               square feet
    ## 27                               square feet
    ## 28                               square feet
    ## 29                               square feet
    ## 30                               square feet
    ## 31                               square feet
    ## 32                                cubic feet
    ## 33                                cubic feet
    ## 34                                cubic feet
    ## 35                                cubic feet
    ## 36                                cubic feet
    ## 37                                cubic feet
    ## 38                                cubic feet
    ## 39                                cubic feet
    ## 40                                cubic feet
    ## 41                                cubic feet
    ## 42                                cubic feet
    ## 43                                cubic feet
    ## 44                                cubic feet
    ## 45                                cubic feet
    ## 46  board feet (International 1/4-inch rule)
    ## 47  board feet (International 1/4-inch rule)
    ## 48                board feet (Scribner rule)
    ## 49                board feet (Scribner rule)
    ## 50                                cubic feet
    ## 51                                cubic feet
    ## 52                                cubic feet
    ## 53                                cubic feet
    ## 54                                cubic feet
    ## 55                                cubic feet
    ## 56                                cubic feet
    ## 57                                cubic feet
    ## 58                                cubic feet
    ## 59                                cubic feet
    ## 60                                cubic feet
    ## 61                                cubic feet
    ## 62                                cubic feet
    ## 63                                cubic feet
    ## 64                                cubic feet
    ## 65                                cubic feet
    ## 66                                cubic feet
    ## 67                                cubic feet
    ## 68                                cubic feet
    ## 69                                cubic feet
    ## 70                                cubic feet
    ## 71                                cubic feet
    ## 72                                cubic feet
    ## 73                                cubic feet
    ## 74                                cubic feet
    ## 75                                cubic feet
    ## 76                                cubic feet
    ## 77                                cubic feet
    ## 78                                cubic feet
    ## 79                                cubic feet
    ## 80                                cubic feet
    ## 81                                cubic feet
    ## 82                                cubic feet
    ## 83                                cubic feet
    ## 84                                cubic feet
    ## 85                                cubic feet
    ## 86                                cubic feet
    ## 87                                      tons
    ## 88                                      tons
    ## 89                                      tons
    ## 90                                      tons
    ## 91                                      tons
    ## 92                                      tons
    ## 93                                      tons
    ## 94                                      tons
    ## 95                                      tons
    ## 96                                      tons
    ## 97                                      tons
    ## 98                                      tons
    ## 99                                      tons
    ## 100                                     tons
    ## 101                                     tons
    ## 102                                     tons
    ## 103                                     tons
    ## 104                                     tons
    ## 105                                     tons
    ## 106                                     tons
    ## 107                                     tons
    ## 108                                     tons
    ## 109                                     tons
    ## 110                                     tons
    ## 111                                     tons
    ## 112                                     tons
    ## 113                                     tons
    ## 114                                     tons
    ## 115                                     tons
    ## 116                                     tons
    ## 117                                     tons
    ## 118                                     tons
    ## 119                                     tons
    ## 120                                     tons
    ## 121                                     tons
    ## 122                                     tons
    ## 123                                     tons
    ## 124                                     tons
    ## 125                                     tons
    ## 126                                     tons
    ## 127                                     tons
    ## 128                                     tons
    ## 129                                     tons
    ## 130                                     tons
    ## 131                                     tons
    ## 132                                     tons
    ## 133                                     tons
    ## 134                                     tons
    ## 135                                     tons
    ## 136                                     tons
    ## 137                                     tons
    ## 138                                     tons
    ## 139                                     tons
    ## 140                                     tons
    ## 141                                     tons
    ## 142                                     tons
    ## 143                                     tons
    ## 144                                     tons
    ## 145                                     tons
    ## 146                                     tons
    ## 147                                     tons
    ## 148                                     tons
    ## 149                                     tons
    ## 150                                     tons
    ## 151                                     tons
    ## 152                                     tons
    ## 153                                     tons
    ## 154                                     tons
    ## 155                                     tons
    ## 156                                     tons
    ## 157                                     tons
    ## 158                                     tons
    ## 159                                     tons
    ## 160                                     tons
    ## 161                                     tons
    ## 162                                     tons
    ## 163                                     tons
    ## 164                                     tons
    ## 165                                     tons
    ## 166                                     tons
    ## 167                                     tons
    ## 168                                     tons
    ## 169                                     tons
    ## 170                                     tons
    ## 171                                    years
    ## 172                                seedlings
    ## 173                                seedlings
    ## 174                                seedlings
    ## 175                                    trees
    ## 176                                    trees
    ## 177                                    trees
    ## 178                                  percent

#### POP1: 1.1 Net cubic-foot volume of live trees, Wyoming, 2011-2013

View Example

Now, we can generate estimates by estimation unit (i.e., ESTN_UNIT) and sum to population (i.e., WY) with `modGBtree`:

    tree1.1 <- modGBtree(GBpopdat = GBpopdat,               # pop - population calculations
                         landarea = "FOREST",               # est - forest land filter
                         sumunits = TRUE,                   # est - sum estimation units to population
                         estvar = "VOLCFNET",               # est - net cubic-foot volume
                         estvar.filter = "STATUSCD == 1",   # est - live trees only
                         returntitle = TRUE)                # out - return title information

We can now take a look at the output list, estimates and percent sampling errors, raw data, and titles:

    # Look at output list
    names(tree1.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"

    # Estimate and percent sampling error of estimate
    tree1.1$est

output

    ##   TOTAL    Estimate Percent Sampling Error
    ## 1 Total 13516579068                    4.7

    # Raw data (list object) for estimate
    raw1.1 <- tree1.1$raw      # extract raw data list object from output
    names(raw1.1)

output

    ##  [1] "unit_totest"   "totest"        "domdat"        "domdatqry"
    ##  [5] "estvar"        "estvar.filter" "module"        "esttype"
    ##  [9] "GBmethod"      "rowvar"        "colvar"        "areaunits"
    ## [13] "estunits"

    head(raw1.1$unit_totest)   # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat  nhat.var NBRPLT.gt0 AREAUSED       est           est.var
    ## 1         1 182.99248  570.5481         23  2757613 504622439  4338692600470222
    ## 2        11 332.82332 3942.7626         26  1837124 611437715 13306920568046216
    ## 3        13 135.25819  314.7875         46  5930088 802092998 11069798005366984
    ## 4        15   0.00000    0.0000          0  1428579         0                 0
    ## 5        17  50.79925 1750.3331          5  1283969  65224666  2885557842098307
    ## 6        19 282.93497 3261.2022         21  2671802 755946217 23280176331001500
    ##      est.se    est.cv      pse  CI99left  CI99right  CI95left  CI95right
    ## 1  65868753 0.1305308 13.05308 334955776  674289103 375522056  633722823
    ## 2 115355627 0.1886629 18.86629 314301311  908574118 385344841  837530588
    ## 3 105213108 0.1311732 13.11732 531081993 1073104004 595879097 1008306900
    ## 4         0       NaN      NaN         0          0         0          0
    ## 5  53717389 0.8235748 82.35748         0  203591490         0  170508813
    ## 6 152578427 0.2018377 20.18377 362930234 1148962200 456897995 1054994438
    ##    CI68left CI68right NBRPLT
    ## 1 439118739 570126140    133
    ## 2 496721402 726154027     85
    ## 3 697462994 906723002    290
    ## 4         0         0     70
    ## 5  11804985 118644346     58
    ## 6 604213397 907679036    128

    head(raw1.1$totest)        # estimates for population (i.e., WY)

output

    ##   TOTAL         est            est.var NBRPLT.gt0 AREAUSED    est.se     est.cv
    ## 1     1 13516579068 402757590627440448        470 62600430 634631854 0.04695211
    ##        pse    CI99left   CI99right    CI95left   CI95right    CI68left
    ## 1 4.695211 11881875741 15151282396 12272723490 14760434646 12885464418
    ##     CI68right NBRPLT
    ## 1 14147693719   3047

    # Titles (list object) for estimate
    titlelst1.1 <- tree1.1$titlelst
    names(titlelst1.1)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.unitvar"
    ##  [5] "title.ref"     "outfn.estpse"  "outfn.rawdat"  "outfn.param"
    ##  [9] "title.tot"     "title.unit"

    titlelst1.1

output

    ## $title.estpse
    ## [1] "VOLCFNET_TPA_ADJ_live, in cubic feet, and percent sampling error on forest land"
    ##
    ## $title.yvar
    ## [1] ", in cubic feet"
    ##
    ## $title.estvar
    ## [1] "VOLCFNET_TPA_ADJ_live"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] ""
    ##
    ## $outfn.estpse
    ## [1] "tree_VOLCFNET_TPA_ADJ_live_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "tree_VOLCFNET_TPA_ADJ_live_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "tree_VOLCFNET_TPA_ADJ_live_forestland_parameters"
    ##
    ## $title.tot
    ## [1] "VOLCFNET_TPA_ADJ_live, in cubic feet, on forest land"
    ##
    ## $title.unit
    ## [1] "cubic feet"

#### POP1: 1.2 Net cubic-foot volume of live trees by forest type, Wyoming, 2011-2013

View Example

This example adds rows to the output for net cubic-foot volume of live trees (at least 5 inches diameter) by forest type, Wyoming, 2011-2013. We also choose to return titles with `returntitle = TRUE`.

    tree1.2 <- modGBtree(GBpopdat = GBpopdat,               # pop - population calculations
                         landarea = "FOREST",               # est - forest land filter
                         sumunits = TRUE,                   # est - sum estimation units to population
                         estvar = "VOLCFNET",               # est - net cubic-foot volume
                         estvar.filter = "STATUSCD == 1",   # est - live trees only
                         rowvar = "FORTYPCD",               # est - row domain
                         returntitle = TRUE)                # out - return title information

Again, we investigate the output of the returned list:

    # Look at output list
    names(tree1.2)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"

    # Estimate and percent sampling error of estimate
    tree1.2$est

output

    ##    Forest type      Estimate Percent Sampling Error
    ## 1          182   103705329.9                  42.43
    ## 2          184     7591934.5                  86.68
    ## 3          185            --                     --
    ## 4          201  1279391473.1                  20.05
    ## 5          221    1134539402                  15.44
    ## 6          265  1318783735.9                   27.1
    ## 7          266  3197965334.6                  12.53
    ## 8          268  1543226579.4                  16.43
    ## 9          269    54704348.4                 101.99
    ## 10         281  3899090965.5                  10.64
    ## 11         366    52426047.4                  49.94
    ## 12         367   242115077.6                   27.2
    ## 13         509    32834363.6                  63.49
    ## 14         517     2578979.3                 107.34
    ## 15         703   169546392.1                  58.21
    ## 16         706     4455857.7                    100
    ## 17         901   420518373.4                  27.81
    ## 18         999    53104873.9                  42.69
    ## 19        <NA>            --                     --
    ## 20       Total 13516579068.4                    4.7

    # Raw data (list object) for estimate
    raw1.2 <- tree1.2$raw      # extract raw data list object from output
    names(raw1.2)

output

    ##  [1] "unit_totest"   "totest"        "unit_rowest"   "rowest"
    ##  [5] "domdat"        "domdatqry"     "estvar"        "estvar.filter"
    ##  [9] "module"        "esttype"       "GBmethod"      "rowvar"
    ## [13] "colvar"        "areaunits"     "estunits"

    head(raw1.2$unit_totest)   # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat  nhat.var NBRPLT.gt0 AREAUSED       est           est.var
    ## 1         1 182.99248  570.5481         23  2757613 504622439  4338692600470222
    ## 2        11 332.82332 3942.7626         26  1837124 611437715 13306920568046216
    ## 3        13 135.25819  314.7875         46  5930088 802092998 11069798005366984
    ## 4        15   0.00000    0.0000          0  1428579         0                 0
    ## 5        17  50.79925 1750.3331          5  1283969  65224666  2885557842098307
    ## 6        19 282.93497 3261.2022         21  2671802 755946217 23280176331001500
    ##      est.se    est.cv      pse  CI99left  CI99right  CI95left  CI95right
    ## 1  65868753 0.1305308 13.05308 334955776  674289103 375522056  633722823
    ## 2 115355627 0.1886629 18.86629 314301311  908574118 385344841  837530588
    ## 3 105213108 0.1311732 13.11732 531081993 1073104004 595879097 1008306900
    ## 4         0       NaN      NaN         0          0         0          0
    ## 5  53717389 0.8235748 82.35748         0  203591490         0  170508813
    ## 6 152578427 0.2018377 20.18377 362930234 1148962200 456897995 1054994438
    ##    CI68left CI68right NBRPLT
    ## 1 439118739 570126140    133
    ## 2 496721402 726154027     85
    ## 3 697462994 906723002    290
    ## 4         0         0     70
    ## 5  11804985 118644346     58
    ## 6 604213397 907679036    128

    head(raw1.2$totest)        # estimates for population (i.e., WY)

output

    ##   TOTAL         est            est.var NBRPLT.gt0 AREAUSED    est.se     est.cv
    ## 1     1 13516579068 402757590627440448        470 62600430 634631854 0.04695211
    ##        pse    CI99left   CI99right    CI95left   CI95right    CI68left
    ## 1 4.695211 11881875741 15151282396 12272723490 14760434646 12885464418
    ##     CI68right NBRPLT
    ## 1 14147693719   3047

    head(raw1.2$unit_rowest)   # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT Forest type       nhat    nhat.var NBRPLT.gt0 AREAUSED       est
    ## 1         1         182  0.4333135   0.1979394          1  2757613   1194911
    ## 2         1         201 15.9927786 197.6889704          1  2757613  44101894
    ## 3         1         221 33.6483513 248.1857429          4  2757613  92789131
    ## 4         1         266 66.8269701 727.3782450          4  2757613 184282921
    ## 5         1         268  8.7794104  59.5751697          1  2757613  24210216
    ## 6         1         281 44.1639244 440.8727504          4  2757613 121787012
    ##            est.var   est.se    est.cv       pse CI99left CI99right CI95left
    ## 1    1505216270617  1226873 1.0267481 102.67481        0   4355125        0
    ## 2 1503311829880000 38772565 0.8791587  87.91587        0 143973404        0
    ## 3 1887310973928104 43443192 0.4681927  46.81927        0 204691379  7642038
    ## 4 5531296553302686 74372687 0.4035788  40.35788        0 375854268 38515134
    ## 5  453035175150730 21284623 0.8791587  87.91587        0  79035772        0
    ## 6 3352585730574492 57901518 0.4754326  47.54326        0 270931438  8302123
    ##   CI95right  CI68left CI68right
    ## 1   3599537         0   2414984
    ## 2 120094726   5544211  82659577
    ## 3 177936224  49586706 135991556
    ## 4 330050709 110322417 258243426
    ## 5  65927311   3043555  45376877
    ## 6 235271901  64206392 179367633

    head(raw1.2$rowest)        # estimates by row for population (i.e., WY)

output

    ##   Forest type        est            est.var NBRPLT.gt0    est.se    est.cv
    ## 1         182  103705330   1936588046855594         22  44006682 0.4243435
    ## 2         184    7591935     43305868002617          4   6580719 0.8668040
    ## 3         185          0                  0          0         0       NaN
    ## 4         201 1279391473  65830017927537704         47 256573611 0.2005435
    ## 5         221 1134539402  30684436423946804         51 175169736 0.1543972
    ## 6         265 1318783736 127733572106014096         26 357398338 0.2710060
    ##        pse  CI99left  CI99right  CI95left  CI95right   CI68left  CI68right
    ## 1 42.43435         0  217059031  17453818  189956841   59942538  147468122
    ## 2 86.68040         0   24542744         0   20489908    1047686   14136183
    ## 3      NaN         0          0         0          0          0          0
    ## 4 20.05435 618501647 1940281299 776516436 1782266510 1024239823 1534543123
    ## 5 15.43972 683332063 1585746741 791213028 1477865776  960340477 1308738327
    ## 6 27.10060 398186623 2239380848 618295865 2019271607  963366141 1674201331

    # Titles (list object) for estimate
    titlelst1.2 <- tree1.2$titlelst
    names(titlelst1.2)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.unitvar"
    ##  [5] "title.ref"     "outfn.estpse"  "outfn.rawdat"  "outfn.param"
    ##  [9] "title.rowvar"  "title.row"     "title.unit"

    titlelst1.2

output

    ## $title.estpse
    ## [1] "VOLCFNET_TPA_ADJ_live, in cubic feet, and percent sampling error on forest land by forest type"
    ##
    ## $title.yvar
    ## [1] ", in cubic feet"
    ##
    ## $title.estvar
    ## [1] "VOLCFNET_TPA_ADJ_live"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] ""
    ##
    ## $outfn.estpse
    ## [1] "tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "VOLCFNET_TPA_ADJ_live, in cubic feet, on forest land by forest type"
    ##
    ## $title.unit
    ## [1] "cubic feet"

We can also create a simple barplot from the output:

    datBarplot(raw1.2$unit_rowest,
               xvar = titlelst1.2$title.rowvar,
               yvar = "est")

plot

#### POP1: 1.3 Net cubic-foot volume of live trees by forest type and stand-size class, Wyoming, 2011-2013

View Example

This examples adds rows and columns to the output, including FIA names, for net cubic-foot volume of live trees (at least 5 inches diameter) by forest type and stand-size class, Wyoming, 2011-2013. We also use the `*_options` functions to return output with estimates (est) and percent standard error (pse) in same cell - est(pse) with `allin1 = TRUE` and save data to an outfolder with `savedata = TRUE` and `outfolder = outfolder`.

    tree1.3 <- modGBtree(GBpopdat = GBpopdat,                # pop - population calculations
                         landarea = "FOREST",                # est - forest land filter
                         sumunits = TRUE,                    # est - sum estimation units to population
                         estvar = "VOLCFNET",                # est - net cubic-foot volume
                         estvar.filter = "STATUSCD  == 1",   # est - live trees only
                         rowvar = "FORTYPCD",                # est - row domain
                         colvar = "STDSZCD",                 # est - column domain
                         returntitle = TRUE,                 # out - return title information
                         savedata = TRUE,                    # out - save data to outfolder
                         table_opts = table_options(
                           row.FIAname = TRUE,               # est - row domain names
                           col.FIAname = TRUE,               # est - column domain names
                           allin1 = TRUE                     # out - return output with est(pse)
                         ),
                         savedata_opts = savedata_options(
                           outfolder = outfolder,            # out - outfolder for saving data
                           outfn.pre = "WY"                  # out - prefix for output files
                         ))

Again, we investigate the output of the returned list:

    # Look at output list from modGBarea()
    names(tree1.3)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"

    # Estimate and percent sampling error of estimate
    tree1.3$est

output

    ##                                 Forest type            Large diameter
    ## 1                    Rocky Mountain juniper     85,055,988.6 ( 49.82)
    ## 2                          Juniper woodland      7,591,934.5 ( 86.68)
    ## 3                 Pinyon / juniper woodland               -- (    --)
    ## 4                               Douglas-fir  1,140,205,175.8 ( 22.15)
    ## 5                            Ponderosa pine  1,074,972,177.1 ( 16.31)
    ## 6                          Engelmann spruce  1,148,600,635.6 ( 30.12)
    ## 7          Engelmann spruce / subalpine fir  2,595,233,199.9 ( 14.80)
    ## 8                             Subalpine fir  1,245,621,714.4 ( 19.26)
    ## 9                               Blue spruce     54,704,348.4 (101.99)
    ## 10                           Lodgepole pine  2,307,223,821.3 ( 15.56)
    ## 11                              Limber pine     37,629,737.1 ( 67.46)
    ## 12                           Whitebark pine    118,870,056.2 ( 45.01)
    ## 13                                  Bur oak     18,577,479.5 (107.34)
    ## 14                 Elm / ash / black locust               -- (    --)
    ## 15                               Cottonwood    164,843,686.0 ( 59.83)
    ## 16 Sugarberry / hackberry / elm / green ash               -- (    --)
    ## 17                                    Aspen     70,340,496.6 ( 49.87)
    ## 18                               Nonstocked               -- (    --)
    ## 19                                    Other               -- (    --)
    ## 20                                    Total 10,069,470,451.1 (  6.36)
    ##             Medium diameter         Small diameter            Nonstocked
    ## 1     16,736,394.2 ( 70.48)   1,912,947.0 ( 89.45)           -- (    --)
    ## 2               -- (    --)            -- (    --)           -- (    --)
    ## 3               -- (    --)            -- (    --)           -- (    --)
    ## 4     94,302,692.8 ( 46.03)  44,883,604.5 ( 51.47)           -- (    --)
    ## 5     52,603,087.3 ( 66.66)   6,964,137.6 ( 84.92)           -- (    --)
    ## 6    137,667,904.0 ( 65.78)  32,515,196.4 ( 69.63)           -- (    --)
    ## 7    454,333,764.9 ( 28.34) 148,398,369.8 ( 37.24)           -- (    --)
    ## 8    236,873,679.2 ( 38.35)  60,731,185.8 ( 40.74)           -- (    --)
    ## 9               -- (    --)            -- (    --)           -- (    --)
    ## 10 1,414,909,655.2 ( 16.23) 176,957,489.0 ( 29.11)           -- (    --)
    ## 11              -- (    --)  14,796,310.4 ( 44.11)           -- (    --)
    ## 12    83,010,320.7 ( 43.06)  40,234,700.7 ( 49.07)           -- (    --)
    ## 13     7,750,865.8 ( 72.26)   6,506,018.4 ( 74.53)           -- (    --)
    ## 14              -- (    --)   2,578,979.3 (107.34)           -- (    --)
    ## 15     2,057,215.9 (102.34)   2,645,490.2 (102.13)           -- (    --)
    ## 16     4,455,857.7 (100.00)            -- (    --)           -- (    --)
    ## 17   240,712,660.9 ( 44.47) 109,465,215.8 ( 32.21)           -- (    --)
    ## 18              -- (    --)            -- (    --) 53,104,873.9 ( 42.69)
    ## 19              -- (    --)            -- (    --)           -- (    --)
    ## 20 2,745,414,098.6 ( 11.08) 648,589,644.9 ( 14.26) 53,104,873.9 ( 42.69)
    ##                        Total
    ## 1     420,518,373.4 ( 27.81)
    ## 2      54,704,348.4 (101.99)
    ## 3      32,834,363.6 ( 63.49)
    ## 4     169,546,392.1 ( 58.21)
    ## 5   1,279,391,473.1 ( 20.05)
    ## 6       2,578,979.3 (107.34)
    ## 7   1,318,783,735.9 ( 27.10)
    ## 8   3,197,965,334.6 ( 12.53)
    ## 9       7,591,934.5 ( 86.68)
    ## 10     52,426,047.4 ( 49.94)
    ## 11  3,899,090,965.5 ( 10.64)
    ## 12     53,104,873.9 ( 42.69)
    ## 13               -- (    --)
    ## 14               -- (    --)
    ## 15  1,134,539,402.0 ( 15.44)
    ## 16    103,705,329.9 ( 42.43)
    ## 17  1,543,226,579.4 ( 16.43)
    ## 18      4,455,857.7 (100.00)
    ## 19    242,115,077.6 ( 27.20)
    ## 20 13,516,579,068.4 (  4.70)

    # Raw data (list object) for estimate
    raw1.3 <- tree1.3$raw      # extract raw data list object from output
    names(raw1.3)

output

    ##  [1] "unit_totest"   "totest"        "unit_rowest"   "rowest"
    ##  [5] "unit_colest"   "colest"        "unit_grpest"   "grpest"
    ##  [9] "domdat"        "domdatqry"     "estvar"        "estvar.filter"
    ## [13] "module"        "esttype"       "GBmethod"      "rowvar"
    ## [17] "colvar"        "areaunits"     "estunits"

    head(raw1.3$unit_totest)   # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat  nhat.var NBRPLT.gt0 AREAUSED       est           est.var
    ## 1         1 182.99248  570.5481         23  2757613 504622439  4338692600470222
    ## 2        11 332.82332 3942.7626         26  1837124 611437715 13306920568046216
    ## 3        13 135.25819  314.7875         46  5930088 802092998 11069798005366984
    ## 4        15   0.00000    0.0000          0  1428579         0                 0
    ## 5        17  50.79925 1750.3331          5  1283969  65224666  2885557842098307
    ## 6        19 282.93497 3261.2022         21  2671802 755946217 23280176331001500
    ##      est.se    est.cv      pse  CI99left  CI99right  CI95left  CI95right
    ## 1  65868753 0.1305308 13.05308 334955776  674289103 375522056  633722823
    ## 2 115355627 0.1886629 18.86629 314301311  908574118 385344841  837530588
    ## 3 105213108 0.1311732 13.11732 531081993 1073104004 595879097 1008306900
    ## 4         0       NaN      NaN         0          0         0          0
    ## 5  53717389 0.8235748 82.35748         0  203591490         0  170508813
    ## 6 152578427 0.2018377 20.18377 362930234 1148962200 456897995 1054994438
    ##    CI68left CI68right NBRPLT
    ## 1 439118739 570126140    133
    ## 2 496721402 726154027     85
    ## 3 697462994 906723002    290
    ## 4         0         0     70
    ## 5  11804985 118644346     58
    ## 6 604213397 907679036    128

    head(raw1.3$totest)        # estimates for population (i.e., WY)

output

    ##   TOTAL         est            est.var NBRPLT.gt0 AREAUSED    est.se     est.cv
    ## 1     1 13516579068 402757590627440448        470 62600430 634631854 0.04695211
    ##        pse    CI99left   CI99right    CI95left   CI95right    CI68left
    ## 1 4.695211 11881875741 15151282396 12272723490 14760434646 12885464418
    ##     CI68right NBRPLT
    ## 1 14147693719   3047

    head(raw1.3$unit_rowest)   # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT FORTYPCD       nhat    nhat.var NBRPLT.gt0
    ## 1         1      182  0.4333135   0.1979394          1
    ## 2         1      201 15.9927786 197.6889704          1
    ## 3         1      221 33.6483513 248.1857429          4
    ## 4         1      266 66.8269701 727.3782450          4
    ## 5         1      268  8.7794104  59.5751697          1
    ## 6         1      281 44.1639244 440.8727504          4
    ##                        Forest type AREAUSED       est          est.var   est.se
    ## 1           Rocky Mountain juniper  2757613   1194911    1505216270617  1226873
    ## 2                      Douglas-fir  2757613  44101894 1503311829880000 38772565
    ## 3                   Ponderosa pine  2757613  92789131 1887310973928104 43443192
    ## 4 Engelmann spruce / subalpine fir  2757613 184282921 5531296553302686 74372687
    ## 5                    Subalpine fir  2757613  24210216  453035175150730 21284623
    ## 6                   Lodgepole pine  2757613 121787012 3352585730574492 57901518
    ##      est.cv       pse CI99left CI99right CI95left CI95right  CI68left CI68right
    ## 1 1.0267481 102.67481        0   4355125        0   3599537         0   2414984
    ## 2 0.8791587  87.91587        0 143973404        0 120094726   5544211  82659577
    ## 3 0.4681927  46.81927        0 204691379  7642038 177936224  49586706 135991556
    ## 4 0.4035788  40.35788        0 375854268 38515134 330050709 110322417 258243426
    ## 5 0.8791587  87.91587        0  79035772        0  65927311   3043555  45376877
    ## 6 0.4754326  47.54326        0 270931438  8302123 235271901  64206392 179367633

    head(raw1.3$rowest)        # estimates by row for population (i.e., WY)

output

    ##                 Forest type        est            est.var NBRPLT.gt0    est.se
    ## 1    Rocky Mountain juniper  103705330   1936588046855594         22  44006682
    ## 2          Juniper woodland    7591935     43305868002617          4   6580719
    ## 3 Pinyon / juniper woodland          0                  0          0         0
    ## 4               Douglas-fir 1279391473  65830017927537704         47 256573611
    ## 5            Ponderosa pine 1134539402  30684436423946804         51 175169736
    ## 6          Engelmann spruce 1318783736 127733572106014096         26 357398338
    ##      est.cv      pse  CI99left  CI99right  CI95left  CI95right   CI68left
    ## 1 0.4243435 42.43435         0  217059031  17453818  189956841   59942538
    ## 2 0.8668040 86.68040         0   24542744         0   20489908    1047686
    ## 3       NaN      NaN         0          0         0          0          0
    ## 4 0.2005435 20.05435 618501647 1940281299 776516436 1782266510 1024239823
    ## 5 0.1543972 15.43972 683332063 1585746741 791213028 1477865776  960340477
    ## 6 0.2710060 27.10060 398186623 2239380848 618295865 2019271607  963366141
    ##    CI68right
    ## 1  147468122
    ## 2   14136183
    ## 3          0
    ## 4 1534543123
    ## 5 1308738327
    ## 6 1674201331

    head(raw1.3$unit_colest)   # estimates by column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT STDSZCD       nhat    nhat.var NBRPLT.gt0 Stand-size class AREAUSED
    ## 1         1       1  88.530660  752.979451          9   Large diameter  2757613
    ## 2         1       2  81.400543  618.813946          6  Medium diameter  2757613
    ## 3         1       3  10.541634   24.150550          6   Small diameter  2757613
    ## 4         1       5   2.519642    5.926747          2       Nonstocked  2757613
    ## 5        11       1 297.218677 4439.407623         18   Large diameter  1837124
    ## 6        11       2  27.239856  336.141484          4  Medium diameter  1837124
    ##         est           est.var    est.se    est.cv      pse  CI99left CI99right
    ## 1 244133298  5725979120537778  75670200 0.3099544 30.99544  49219780 439046816
    ## 2 224471197  4705726997342543  68598302 0.3055996 30.55996  47773681 401168713
    ## 3  29069746   183651154990174  13551795 0.4661821 46.61821         0  63976857
    ## 4   6948198    45069526385061   6713384 0.9662050 96.62050         0  24240730
    ## 5 546027565 14983109899193358 122405514 0.2241746 22.41746 230731854 861323275
    ## 6  50042992  1134485773514754  33682128 0.6730638 67.30638         0 136802406
    ##    CI95left CI95right    CI68left CI68right
    ## 1  95822432 392444164 168882471.4 319384125
    ## 2  90020996 358921398 156253074.9 292689319
    ## 3   2508715  55630777  15593056.4  42546436
    ## 4         0  20106190    272020.7  13624376
    ## 5 306117165 785937964 424300436.0 667754693
    ## 6         0 116058751  16547534.3  83538451

    head(raw1.3$colest)        # estimates by column for population (i.e., WY)

output

    ##   Stand-size class         est            est.var NBRPLT.gt0    est.se
    ## 1   Large diameter 10069470451 409612607025502592        272 640009849
    ## 2  Medium diameter  2745414099  92572401935655280        102 304257131
    ## 3   Small diameter   648589645   8557190054968199         95  92505081
    ## 4       Nonstocked    53104874    514020867451587         17  22672028
    ##       est.cv       pse   CI99left   CI99right   CI95left   CI95right   CI68left
    ## 1 0.06355943  6.355943 8420914327 11718026575 8815074197 11323866705 9433007611
    ## 2 0.11082377 11.082377 1961699664  3529128533 2149081079  3341747118 2442843196
    ## 3 0.14262497 14.262497  410312346   886866944  467283017   829896273  556597238
    ## 4 0.42692933 42.692933          0   111504149    8668515    97541233   30558497
    ##     CI68right
    ## 1 10705933291
    ## 2  3047985001
    ## 3   740582052
    ## 4    75651251

    head(raw1.3$unit_grpest)   # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT FORTYPCD STDSZCD       nhat    nhat.var NBRPLT.gt0 Stand-size class
    ## 1         1      182       1  0.4333135   0.1979394          1   Large diameter
    ## 2         1      201       1 15.9927786 197.6889704          1   Large diameter
    ## 3         1      221       1 33.6483513 248.1857429          4   Large diameter
    ## 4         1      266       1 38.1576244 532.2301642          2   Large diameter
    ## 5         1      266       2 28.6693457 300.8404694          2  Medium diameter
    ## 6         1      268       2  8.7794104  59.5751697          1  Medium diameter
    ##                        Forest type AREAUSED       est          est.var   est.se
    ## 1           Rocky Mountain juniper  2757613   1194911    1505216270617  1226873
    ## 2                      Douglas-fir  2757613  44101894 1503311829880000 38772565
    ## 3                   Ponderosa pine  2757613  92789131 1887310973928104 43443192
    ## 4 Engelmann spruce / subalpine fir  2757613 105223961 4047306738857966 63618447
    ## 5 Engelmann spruce / subalpine fir  2757613  79058960 2287720127679708 47830117
    ## 6                    Subalpine fir  2757613  24210216  453035175150730 21284623
    ##      est.cv       pse CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1 1.0267481 102.67481        0   4355125        0   3599537        0   2414984
    ## 2 0.8791587  87.91587        0 143973404        0 120094726  5544211  82659577
    ## 3 0.4681927  46.81927        0 204691379  7642038 177936224 49586706 135991556
    ## 4 0.6046004  60.46004        0 269094220        0 229913825 41958095 168489827
    ## 5 0.6049930  60.49930        0 202261178        0 172804268 31493923 126623998
    ## 6 0.8791587  87.91587        0  79035772        0  65927311  3043555  45376877

    head(raw1.3$grpest)        # estimates by row and column for population (i.e., WY)

output

    ##                 Forest type Stand-size class      est          est.var
    ## 1    Rocky Mountain juniper   Large diameter 85055989 1795608102610001
    ## 2    Rocky Mountain juniper  Medium diameter 16736394  139127901089290
    ## 3    Rocky Mountain juniper   Small diameter  1912947    2927803832821
    ## 4          Juniper woodland   Large diameter  7591935   43305868002617
    ## 5          Juniper woodland   Small diameter        0                0
    ## 6 Pinyon / juniper woodland   Large diameter        0                0
    ##   NBRPLT.gt0   est.se    est.cv      pse CI99left CI99right CI95left CI95right
    ## 1         17 42374616 0.4981967 49.81967        0 194205767  2003267 168108710
    ## 2          3 11795249 0.7047664 70.47664        0  47118942        0  39854658
    ## 3          2  1711083 0.8944747 89.44747        0   6320404        0   5266607
    ## 4          4  6580719 0.8668040 86.68040        0  24542744        0  20489908
    ## 5          0        0       NaN      NaN        0         0        0         0
    ## 6          0        0       NaN      NaN        0         0        0         0
    ##     CI68left CI68right
    ## 1 42916217.5 127195760
    ## 2  5006515.8  28466273
    ## 3   211347.4   3614547
    ## 4  1047686.2  14136183
    ## 5        0.0         0
    ## 6        0.0         0

    # Titles (list object) for estimate
    titlelst1.3 <- tree1.3$titlelst
    names(titlelst1.3)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.unitvar"
    ##  [5] "title.ref"     "outfn.estpse"  "outfn.rawdat"  "outfn.param"
    ##  [9] "title.rowvar"  "title.row"     "title.colvar"  "title.col"
    ## [13] "title.unit"

    titlelst1.3

output

    ## $title.estpse
    ## [1] "VOLCFNET_TPA_ADJ_live, in cubic feet (percent sampling error), by forest type and stand-size class on forest land"
    ##
    ## $title.yvar
    ## [1] ", in cubic feet"
    ##
    ## $title.estvar
    ## [1] "VOLCFNET_TPA_ADJ_live"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] ""
    ##
    ## $outfn.estpse
    ## [1] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "VOLCFNET_TPA_ADJ_live, in cubic feet (percent sampling error), by forest type on forest land"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "VOLCFNET_TPA_ADJ_live, in cubic feet (percent sampling error), by stand-size class on forest land"
    ##
    ## $title.unit
    ## [1] "cubic feet"

    # List output files in outfolder
    list.files(outfolder, pattern = "WY_tree")

output

    ## [1] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland.csv"

    list.files(paste0(outfolder, "/rawdata"), pattern = "WY_tree")

output

    ## [1] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_colest.csv"
    ## [2] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_domdat.csv"
    ## [3] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_grpest.csv"
    ## [4] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_rowest.csv"
    ## [5] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_totest.csv"
    ## [6] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_colest.csv"
    ## [7] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_grpest.csv"
    ## [8] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_rowest.csv"
    ## [9] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_totest.csv"

#### POP1: 1.4 Number of live trees by species, Wyoming, 2011-2013

View Example

    tree1.4 <- modGBtree(GBpopdat = GBpopdat,                # pop - population calculations
                         landarea = "FOREST",                # est - forest land filter
                         sumunits = TRUE,                    # est - sum estimation units to population
                         estvar = "TPA_UNADJ",               # est - number of trees per acre
                         estvar.filter = "STATUSCD == 1",    # est - live trees only
                         rowvar = "SPCD",                    # est - row domain
                         returntitle = TRUE,                 # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,               # est - row domain names
                           allin1 = FALSE                    # out - return output with est and pse
                         ))

We can also look at the output list and estimates again:

    # Look at output list
    names(tree1.4)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"

    # Estimate and percent sampling error of estimate
    tree1.4$est

output

    ##                        Species     Estimate Percent Sampling Error
    ## 1                subalpine fir 1149064734.8                   8.33
    ## 2                 Utah juniper   45255337.2                  27.67
    ## 3       Rocky Mountain juniper  100653677.7                  20.77
    ## 4             Engelmann spruce  472342495.2                  11.91
    ## 5                  blue spruce    6731266.5                  63.82
    ## 6               whitebark pine  237604255.6                  15.55
    ## 7  common or two-needle pinyon       119195                    100
    ## 8               lodgepole pine   1458559077                  11.36
    ## 9                  limber pine   93252400.8                   20.6
    ## 10              ponderosa pine  260988183.4                  15.71
    ## 11                 Douglas-fir  206129936.1                  19.48
    ## 12                    boxelder    1126869.5                  81.93
    ## 13                 paper birch    1445853.5                 107.34
    ## 14  curlleaf mountain-mahogany     538260.9                  69.47
    ## 15                   green ash    1288927.3                  91.98
    ## 16           plains cottonwood    2027267.7                  82.62
    ## 17               quaking aspen  253876469.7                  18.88
    ## 18       narrowleaf cottonwood    4585746.2                   57.8
    ## 19                     bur oak   23604682.5                   38.4
    ## 20                       Other           --                     --
    ## 21                       Total 4319194636.6                   4.83

#### POP1: 1.5 Number of live trees (including seedlings) by species, Wyoming, 2011-2013

View Example

We can also add seedlings.

Note: seedling data are only available for estimating number of trees (estvar = TPA_UNADJ).

Note: must include seedling data in population data calculations.

    tree1.5 <- modGBtree(GBpopdat = GBpopdat,                # pop - population calculations
                         estseed = "add",                    # est - add seedling data
                         landarea = "FOREST",                # est - forest land filter
                         sumunits = TRUE,                    # est - sum estimation units to population
                         estvar = "TPA_UNADJ",               # est - number of trees per acre
                         estvar.filter = "STATUSCD == 1",    # est - live trees only
                         rowvar = "SPCD",                    # est - row domain
                         returntitle = TRUE,                 # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,               # est - row domain names
                           allin1 = FALSE,                   # out - return output with est and pse
                         ))

And again we can look at our outputs and compare estimates:

    # Look at output list
    names(tree1.5)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"

    # Estimate and percent sampling error of estimate
    tree1.5$est

output

    ##                        Species      Estimate Percent Sampling Error
    ## 1                subalpine fir  7595942257.9                   8.12
    ## 2                 Utah juniper    51536534.9                  27.08
    ## 3       Rocky Mountain juniper   149789362.2                  20.65
    ## 4             Engelmann spruce  1653336667.9                   10.4
    ## 5                  blue spruce     9597932.4                  72.35
    ## 6               whitebark pine  1409480053.3                  14.49
    ## 7  common or two-needle pinyon        119195                    100
    ## 8               lodgepole pine  4417708153.6                  14.09
    ## 9                  limber pine     465590560                  19.17
    ## 10              ponderosa pine     628036829                  21.73
    ## 11                 Douglas-fir   547823866.2                  24.23
    ## 12                    boxelder     5891677.2                  96.11
    ## 13                 paper birch      10120975                   75.3
    ## 14  curlleaf mountain-mahogany      538260.9                  69.47
    ## 15                   green ash     4180634.3                  79.48
    ## 16           plains cottonwood     2027267.7                  82.62
    ## 17               quaking aspen  1870428252.5                  16.35
    ## 18       narrowleaf cottonwood    51593597.3                  61.75
    ## 19                     bur oak   367717832.4                  50.87
    ## 20                       Other            --                     --
    ## 21                       Total 19241459909.8                   5.21

    # Compare estimates with and without seedlings
    head(tree1.4$est)

output

    ##                  Species     Estimate Percent Sampling Error
    ## 1          subalpine fir 1149064734.8                   8.33
    ## 2           Utah juniper   45255337.2                  27.67
    ## 3 Rocky Mountain juniper  100653677.7                  20.77
    ## 4       Engelmann spruce  472342495.2                  11.91
    ## 5            blue spruce    6731266.5                  63.82
    ## 6         whitebark pine  237604255.6                  15.55

    head(tree1.5$est)

output

    ##                  Species     Estimate Percent Sampling Error
    ## 1          subalpine fir 7595942257.9                   8.12
    ## 2           Utah juniper   51536534.9                  27.08
    ## 3 Rocky Mountain juniper  149789362.2                  20.65
    ## 4       Engelmann spruce 1653336667.9                   10.4
    ## 5            blue spruce    9597932.4                  72.35
    ## 6         whitebark pine 1409480053.3                  14.49

#### POP1: 1.6 Number of seedlings by species, Wyoming, 2011-2013

View Example

Of course, we can also look at only seedlings.

    tree1.6 <- modGBtree(GBpopdat = GBpopdat,           # pop - population calculations
                         estseed = "only",              # est - add seedling data
                         landarea = "FOREST",           # est - forest land filter
                         sumunits = TRUE,               # est - sum estimation units to population
                         estvar = "TPA_UNADJ",          # est - number of trees per acre
                         rowvar = "SPCD",               # est - row domain
                         returntitle = TRUE,            # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,          # est - row domain names
                           allin1 = FALSE               # out - return output with est and pse
                         ))

And again we can look at our outputs and compare estimates:

    # Look at output list
    names(tree1.6)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"

    # Estimate and percent sampling error of estimate
    tree1.6$est

output

    ##                                          Species      Estimate
    ## 1               subalpine fir (Abies lasiocarpa)  6446877524.5
    ## 2           Utah juniper (Juniperus osteosperma)     6281197.2
    ## 3  Rocky Mountain juniper (Juniperus scopulorum)    49135684.6
    ## 4           Engelmann spruce (Picea engelmannii)  1180994171.7
    ## 5                    blue spruce (Picea pungens)     2866665.9
    ## 6              whitebark pine (Pinus albicaulis)  1171875796.9
    ## 7                lodgepole pine (Pinus contorta)  2959149075.2
    ## 8                   limber pine (Pinus flexilis)   372338158.8
    ## 9               ponderosa pine (Pinus ponderosa)   367048644.9
    ## 10           Douglas-fir (Pseudotsuga menziesii)   341693928.8
    ## 11                       boxelder (Acer negundo)     4764807.8
    ## 12               paper birch (Betula papyrifera)     8675121.5
    ## 13            green ash (Fraxinus pennsylvanica)       2891707
    ## 14           quaking aspen (Populus tremuloides)  1616551782.8
    ## 15  narrowleaf cottonwood (Populus angustifolia)      47007851
    ## 16                  bur oak (Quercus macrocarpa)   344113149.7
    ## 17                                         Other            --
    ## 18                                         Total 14922265268.5
    ##    Percent Sampling Error
    ## 1                    8.72
    ## 2                   49.99
    ## 3                   32.32
    ## 4                   11.58
    ## 5                  101.99
    ## 6                   15.95
    ## 7                   17.66
    ## 8                   20.34
    ## 9                   32.58
    ## 10                  33.54
    ## 11                    100
    ## 12                  74.48
    ## 13                 107.34
    ## 14                  17.32
    ## 15                  64.53
    ## 16                     53
    ## 17                     --
    ## 18                   5.99

    # Compare estimates with, without, and only seedlings
    head(tree1.4$est)

output

    ##                  Species     Estimate Percent Sampling Error
    ## 1          subalpine fir 1149064734.8                   8.33
    ## 2           Utah juniper   45255337.2                  27.67
    ## 3 Rocky Mountain juniper  100653677.7                  20.77
    ## 4       Engelmann spruce  472342495.2                  11.91
    ## 5            blue spruce    6731266.5                  63.82
    ## 6         whitebark pine  237604255.6                  15.55

    head(tree1.5$est)

output

    ##                  Species     Estimate Percent Sampling Error
    ## 1          subalpine fir 7595942257.9                   8.12
    ## 2           Utah juniper   51536534.9                  27.08
    ## 3 Rocky Mountain juniper  149789362.2                  20.65
    ## 4       Engelmann spruce 1653336667.9                   10.4
    ## 5            blue spruce    9597932.4                  72.35
    ## 6         whitebark pine 1409480053.3                  14.49

    head(tree1.6$est)

output

    ##                                         Species     Estimate
    ## 1              subalpine fir (Abies lasiocarpa) 6446877524.5
    ## 2          Utah juniper (Juniperus osteosperma)    6281197.2
    ## 3 Rocky Mountain juniper (Juniperus scopulorum)   49135684.6
    ## 4          Engelmann spruce (Picea engelmannii) 1180994171.7
    ## 5                   blue spruce (Picea pungens)    2866665.9
    ## 6             whitebark pine (Pinus albicaulis) 1171875796.9
    ##   Percent Sampling Error
    ## 1                   8.72
    ## 2                  49.99
    ## 3                  32.32
    ## 4                  11.58
    ## 5                 101.99
    ## 6                  15.95

#### POP2: 2.1 Number of live trees by forest type and species on forest land, Bighorn National Forest

View Example

    tree2.1 <- modGBtree(GBpopdat = GBpopdat.bh,             # pop - population calculations
                         landarea = "FOREST",                # est - forest land filter
                         sumunits = TRUE,                    # est - sum estimation units to population
                         estvar = "TPA_UNADJ",               # est - number of trees per acre
                         estvar.filter = "STATUSCD == 1",    # est - live trees only
                         rowvar = "FORTYPCD",                # est - row domain
                         colvar = "SPCD",                    # est - column domain
                         returntitle = TRUE,                 # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,               # est - row domain names
                           col.FIAname = TRUE,               # est - column domain names
                           allin1 = TRUE                     # out - return output with est(pse)
                         ))

And we can see our output:

    # Look at output list
    names(tree2.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"   "invyr"

    # Estimate and percent sampling error of estimate
    head(tree2.1$est)

output

    ##                        Forest type         subalpine fir Rocky Mountain juniper
    ## 1                      Douglas-fir           -- (    --)            -- (    --)
    ## 2                 Engelmann spruce  5,748,668.0 ( 77.02)            -- (    --)
    ## 3 Engelmann spruce / subalpine fir 27,057,507.4 ( 64.52)            -- (    --)
    ## 4                    Subalpine fir 11,180,825.6 ( 86.24)            -- (    --)
    ## 5                   Lodgepole pine 27,362,519.2 ( 36.76)            -- (    --)
    ## 6                            Aspen           -- (    --)            -- (    --)
    ##        Engelmann spruce         lodgepole pine          limber pine
    ## 1           -- (    --)            -- (    --) 4,283,555.1 ( 97.92)
    ## 2 13,619,359.8 ( 52.63)   4,641,377.9 ( 98.14)          -- (    --)
    ## 3 12,145,337.2 ( 60.75)            -- (    --)   357,822.7 (100.76)
    ## 4    119,274.3 (100.76)   2,504,759.0 ( 79.85)          -- (    --)
    ## 5 28,612,774.3 ( 44.96) 158,998,728.3 ( 22.59)          -- (    --)
    ## 6           -- (    --)  11,886,153.3 (100.76)          -- (    --)
    ##             Douglas-fir         quaking aspen       Other
    ## 1 21,223,387.2 ( 89.14)           -- (    --) -- (    --)
    ## 2  3,513,431.2 (100.76)           -- (    --) -- (    --)
    ## 3  1,431,290.9 ( 79.06)           -- (    --) -- (    --)
    ## 4           -- (    --)           -- (    --) -- (    --)
    ## 5    238,548.4 (100.76) 10,052,879.2 ( 84.38) -- (    --)
    ## 6           -- (    --)           -- (    --) -- (    --)
    ##                    Total
    ## 1  11,886,153.3 (100.76)
    ## 2  25,506,942.3 ( 90.53)
    ## 3  27,522,836.9 ( 50.80)
    ## 4  40,991,958.3 ( 55.71)
    ## 5 225,265,449.4 ( 19.47)
    ## 6            -- (    --)

#### POP2: 2.2 Net cubic-foot volume of standing dead trees by species and cause of death on forest land, Bighorn National Forest

View Example

We can also examine dead trees with the filter `estvar.filter = "STATUSCD == 2 & STANDING_DEAD_CD == 1"`.

    deadtree.filter <- "STATUSCD == 2 & STANDING_DEAD_CD == 1"
    tree2.2 <- modGBtree(GBpopdat = GBpopdat.bh,                  # pop - population calculations
                         landarea = "FOREST",                     # est - forest land filter
                         sumunits = TRUE,                         # est - sum estimation units to population
                         estvar = "VOLCFNET",                     # est - number of trees per acre
                         estvar.filter = deadtree.filter,         # est - standing dead trees only
                         rowvar = "SPCD",                         # est - row domain
                         colvar = "AGENTCD",                      # est - column domain
                         returntitle = TRUE,                      # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,                    # est - row domain names
                           col.FIAname = TRUE,                    # est - column domain names
                           allin1 = TRUE                          # out - return output with est(pse)
                         ))

And we can see our output of dead trees:

    # Look at output list
    names(tree2.2)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"   "invyr"

    # Estimate and percent sampling error of estimate
    head(tree2.2$est)

output

    ##                  Species                Insect               Disease
    ## 1          subalpine fir  2,982,393.6 (100.76) 28,505,537.5 ( 62.63)
    ## 2 Rocky Mountain juniper           -- (    --)           -- (    --)
    ## 3       Engelmann spruce 47,430,545.1 ( 97.66)  3,821,319.6 ( 75.96)
    ## 4         lodgepole pine  3,819,221.4 ( 62.70)  1,334,682.8 (100.76)
    ## 5            limber pine           -- (    --)           -- (    --)
    ## 6            Douglas-fir           -- (    --)           -- (    --)
    ##                    Fire  Unidentified animal            Weather
    ## 1  2,629,756.0 (101.19)          -- (    --) 573,086.4 (101.19)
    ## 2           -- (    --)          -- (    --)        -- (    --)
    ## 3 29,761,541.2 (101.19)          -- (    --) 937,611.0 (100.76)
    ## 4           -- (    --) 2,740,770.5 ( 84.11) 904,673.0 ( 77.35)
    ## 5  2,363,965.1 (100.76)          -- (    --)        -- (    --)
    ## 6           -- (    --)          -- (    --)        -- (    --)
    ##   Vegetation (e.g., suppression)          Unidentified       Other
    ## 1             293,076.1 (100.76)  9,225,808.8 ( 66.35) -- (    --)
    ## 2                    -- (    --)           -- (    --) -- (    --)
    ## 3                    -- (    --)  7,016,945.1 ( 75.36) -- (    --)
    ## 4                    -- (    --) 12,381,170.2 ( 55.63) -- (    --)
    ## 5                    -- (    --)    141,365.1 (100.76) -- (    --)
    ## 6                    -- (    --)           -- (    --) -- (    --)
    ##                   Total
    ## 1           -- (    --)
    ## 2 21,180,518.0 ( 43.17)
    ## 3 88,967,962.0 ( 63.07)
    ## 4           -- (    --)
    ## 5  2,505,330.2 ( 95.10)
    ## 6           -- (    --)

#### Adding diameter classes to population tree data (for `modGBtree` and `modGBratio` examples 7-9)

View Example

    # Look at tree data in GBpopdat.bh
    head(GBpopdat.bh$treex)

output

    ## Key: <PLT_CN, CONDID, SUBP, TREE>
    ##            PLT_CN CONDID  SUBP  TREE STATUSCD  SPCD SPGRPCD   DIA    HT
    ##            <char>  <num> <num> <num>    <num> <num>   <num> <num> <num>
    ## 1: 40404876010690      1     1     2        1   108      21  10.2    81
    ## 2: 40404876010690      1     1     3        1   108      21   9.1    49
    ## 3: 40404876010690      1     1     5        1   108      21  10.0    61
    ## 4: 40404876010690      1     1     7        1   108      21  12.9    71
    ## 5: 40404876010690      1     1     9        1    19      12  12.1    64
    ## 6: 40404876010690      1     1    14        1   108      21   6.3    29
    ##    TREECLCD AGENTCD STANDING_DEAD_CD  VOLCFNET  VOLCFGRS  VOLBFNET TPA_UNADJ
    ##       <num>   <num>            <num>     <num>     <num>     <num>     <num>
    ## 1:        2      NA               NA 19.676475 19.676475 100.93822  6.018046
    ## 2:        2      NA               NA  8.852893  8.852893  34.97113  6.018046
    ## 3:        2      NA               NA 13.668155 13.668155  63.82976  6.018046
    ## 4:        2      NA               NA 26.086436 26.086436 149.04320  6.018046
    ## 5:        2      NA               NA 19.055442 19.055442 104.72982  6.018046
    ## 6:        2      NA               NA  2.197854  2.197854        NA  6.018046
    ##    DRYBIO_AG CARBON_AG  tadjfac
    ##        <num>     <num>    <num>
    ## 1:  688.6915 346.41183 1.006135
    ## 2:  366.9639 184.58284 1.006135
    ## 3:  529.3556 266.26586 1.006135
    ## 4: 1008.6610 507.35649 1.006135
    ## 5:  675.8936 325.78072 1.006135
    ## 6:  113.8587  57.27094 1.006135

    # Use reference data frame stored as an R object in FIESTA
    head(FIESTAutils::ref_diacl2in)

output

    ##   MIN  MAX  DIACL2IN
    ## 1   1  2.9   1.0-2.9
    ## 2   3  4.9   3.0-4.9
    ## 3   5  6.9   5.0-6.9
    ## 4   7  8.9   7.0-8.9
    ## 5   9 10.9  9.0-10.9
    ## 6  11 12.9 11.0-12.9

    # Appends a new column to GBpopdat$treex classifying the DIA variable based on MIN and MAX columns in ref_diacl2in
    dat <- datLUTclass(x = GBpopdat.bh$treex,
                       xvar = "DIA",
                       LUT = FIESTAutils::ref_diacl2in,
                       LUTclassnm = "DIACL2IN")

    GBpopdat.bh$treex <- dat$xLUT

    # Look at tree data, with new column (DIACL2IN)
    head(GBpopdat.bh$treex)

output

    ##           PLT_CN CONDID SUBP TREE STATUSCD SPCD SPGRPCD  DIA HT TREECLCD
    ## 1 40404876010690      1    1    2        1  108      21 10.2 81        2
    ## 2 40404876010690      1    1    3        1  108      21  9.1 49        2
    ## 3 40404876010690      1    1    5        1  108      21 10.0 61        2
    ## 4 40404876010690      1    1    7        1  108      21 12.9 71        2
    ## 5 40404876010690      1    1    9        1   19      12 12.1 64        2
    ## 6 40404876010690      1    1   14        1  108      21  6.3 29        2
    ##   AGENTCD STANDING_DEAD_CD  VOLCFNET  VOLCFGRS  VOLBFNET TPA_UNADJ DRYBIO_AG
    ## 1      NA               NA 19.676475 19.676475 100.93822  6.018046  688.6915
    ## 2      NA               NA  8.852893  8.852893  34.97113  6.018046  366.9639
    ## 3      NA               NA 13.668155 13.668155  63.82976  6.018046  529.3556
    ## 4      NA               NA 26.086436 26.086436 149.04320  6.018046 1008.6610
    ## 5      NA               NA 19.055442 19.055442 104.72982  6.018046  675.8936
    ## 6      NA               NA  2.197854  2.197854        NA  6.018046  113.8587
    ##   CARBON_AG  tadjfac  DIACL2IN
    ## 1 346.41183 1.006135  9.0-10.9
    ## 2 184.58284 1.006135  9.0-10.9
    ## 3 266.26586 1.006135  9.0-10.9
    ## 4 507.35649 1.006135 11.0-12.9
    ## 5 325.78072 1.006135 11.0-12.9
    ## 6  57.27094 1.006135   5.0-6.9

    # Look at table of new diameter classes (DIACL2IN)
    table(GBpopdat.bh$treex$DIACL2IN)

output

    ##
    ##   1.0-2.9   3.0-4.9   5.0-6.9   7.0-8.9  9.0-10.9 11.0-12.9 13.0-14.9 15.0-16.9
    ##        82        49       602       450       222       121        62        29
    ## 17.0-18.9 19.0-20.9 21.0-22.9 23.0-24.9 25.0-26.9 27.0-28.9 29.0-30.9 31.0-32.9
    ##        25        17         7         5         1         1         0         0
    ## 33.0-34.9 35.0-36.9 37.0-38.9 39.0-40.9 41.0-42.9 43.0-44.9 45.0-46.9 47.0-48.9
    ##         0         0         0         0         0         0         0         0
    ## 49.0-50.9 51.0-52.9 53.0-54.9 55.0-56.9 57.0-58.9 59.0-60.9 61.0-62.9 63.0-64.9
    ##         0         0         0         0         0         0         0         0
    ## 65.0-66.9 67.0-68.9 69.0-70.9 71.0-72.9 73.0-74.9 75.0-76.9 77.0-78.9 79.0-80.9
    ##         0         0         0         0         0         0         0         0

    # Another way to append diameter classes
    # First, create a new variable using cut function to define 4 diameter classes
    dat <- datLUTclass(x = GBpopdat.bh$treex,
                       xvar = "DIA",
                       cutbreaks = c(0, 5, 10, 20, 100))

    GBpopdat.bh$treex <- dat$xLUT

    # Look at tree data, with new column (DIACL2IN)
    head(GBpopdat.bh$treex)

output

    ##           PLT_CN CONDID SUBP TREE STATUSCD SPCD SPGRPCD  DIA HT TREECLCD
    ## 1 40404876010690      1    1    2        1  108      21 10.2 81        2
    ## 2 40404876010690      1    1    3        1  108      21  9.1 49        2
    ## 3 40404876010690      1    1    5        1  108      21 10.0 61        2
    ## 4 40404876010690      1    1    7        1  108      21 12.9 71        2
    ## 5 40404876010690      1    1    9        1   19      12 12.1 64        2
    ## 6 40404876010690      1    1   14        1  108      21  6.3 29        2
    ##   AGENTCD STANDING_DEAD_CD  VOLCFNET  VOLCFGRS  VOLBFNET TPA_UNADJ DRYBIO_AG
    ## 1      NA               NA 19.676475 19.676475 100.93822  6.018046  688.6915
    ## 2      NA               NA  8.852893  8.852893  34.97113  6.018046  366.9639
    ## 3      NA               NA 13.668155 13.668155  63.82976  6.018046  529.3556
    ## 4      NA               NA 26.086436 26.086436 149.04320  6.018046 1008.6610
    ## 5      NA               NA 19.055442 19.055442 104.72982  6.018046  675.8936
    ## 6      NA               NA  2.197854  2.197854        NA  6.018046  113.8587
    ##   CARBON_AG  tadjfac  DIACL2IN   DIACL
    ## 1 346.41183 1.006135  9.0-10.9 10-19.9
    ## 2 184.58284 1.006135  9.0-10.9   5-9.9
    ## 3 266.26586 1.006135  9.0-10.9 10-19.9
    ## 4 507.35649 1.006135 11.0-12.9 10-19.9
    ## 5 325.78072 1.006135 11.0-12.9 10-19.9
    ## 6  57.27094 1.006135   5.0-6.9   5-9.9

    # Look at table of new diameter classes (DIACL)
    table(GBpopdat.bh$treex$DIACL)

output

    ##
    ##   0-4.9   5-9.9 10-19.9     20+
    ##     131    1177     344      21

#### POP2: 2.3 Number of Live Trees by Species Groups and Diameter Class on forest land, Bighorn National Forest

View Example

We can also look at the number of live trees by species group and diameter class (DIACL2IN):

    tree2.3 <- modGBtree(GBpopdat = GBpopdat.bh,             # pop - population calculations
                         landarea = "FOREST",                # est - forest land filter
                         sumunits = TRUE,                    # est - sum estimation units to population
                         estvar = "TPA_UNADJ",               # est - number of trees per acre
                         estvar.filter = "STATUSCD == 1",    # est - live trees only
                         rowvar = "SPGRPCD",                 # est - row domain
                         colvar = "DIACL2IN",                # est - column domain
                         returntitle = TRUE,                 # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,               # est - row domain names
                           allin1 = TRUE                     # out - return output with est(pse)
                         ))

Outputs:

    # Look at output list
    names(tree2.3)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"   "invyr"

    # Estimate and percent sampling error of estimate
    head(tree2.3$est)

output

    ##                 Species group               1.0-2.9            11.0-12.9
    ## 1                 Douglas-fir 14,857,691.8 (100.76)   119,274.3 (100.76)
    ## 2                    True fir 35,733,107.9 ( 34.02) 1,318,009.6 ( 44.43)
    ## 3 Engelmann and other spruces 19,538,942.7 ( 52.05) 4,067,309.8 ( 28.16)
    ## 4              Lodgepole pine 43,087,305.9 ( 36.30) 7,168,439.9 ( 22.35)
    ## 5          Woodland softwoods           -- (    --)          -- (    --)
    ## 6     Other western softwoods  2,971,538.3 (100.76)          -- (    --)
    ##              13.0-14.9            15.0-16.9          17.0-18.9
    ## 1   357,822.7 ( 74.35)   357,822.7 (100.76)        -- (    --)
    ## 2   119,274.3 (100.76)   122,270.6 (101.19) 119,274.3 (100.76)
    ## 3 2,045,640.1 ( 39.54)   834,919.8 ( 64.85) 957,190.4 ( 55.17)
    ## 4 3,226,397.2 ( 30.61) 1,076,464.5 ( 41.49) 834,919.8 ( 74.03)
    ## 5          -- (    --)          -- (    --)        -- (    --)
    ## 6          -- (    --)          -- (    --)        -- (    --)
    ##              19.0-20.9          21.0-22.9          23.0-24.9   25.0-26.9
    ## 1   119,274.3 (100.76) 119,274.3 (100.76) 119,274.3 (100.76) -- (    --)
    ## 2          -- (    --)        -- (    --)        -- (    --) -- (    --)
    ## 3 1,079,460.8 ( 42.09) 119,274.3 (100.76) 244,541.0 (101.19) -- (    --)
    ## 4   357,822.7 ( 74.35)        -- (    --) 119,274.3 (100.76) -- (    --)
    ## 5          -- (    --)        -- (    --)        -- (    --) -- (    --)
    ## 6          -- (    --)        -- (    --)        -- (    --) -- (    --)
    ##     27.0-28.9               3.0-4.9               5.0-6.9               7.0-8.9
    ## 1 -- (    --)  5,943,076.6 ( 60.40)  2,146,936.4 ( 46.77)  1,550,565.3 ( 60.21)
    ## 2 -- (    --) 23,772,306.9 ( 41.20)  6,461,783.1 ( 32.38)  2,862,582.3 ( 36.05)
    ## 3 -- (    --)  7,428,845.7 ( 59.10)  8,247,900.7 ( 29.96)  6,458,787.2 ( 34.84)
    ## 4 -- (    --) 34,284,662.9 ( 34.68) 37,267,497.1 ( 24.24) 32,818,394.4 ( 21.39)
    ## 5 -- (    --)           -- (    --)           -- (    --)           -- (    --)
    ## 6 -- (    --)           -- (    --)  1,192,742.3 ( 82.60)    357,822.7 ( 74.35)
    ##                9.0-10.9          NA                  Total
    ## 1    715,645.7 ( 56.70) -- (    --)            -- (    --)
    ## 2    840,912.8 ( 36.35) -- (    --)  26,406,658.4 ( 72.43)
    ## 3  3,473,934.7 ( 36.36) -- (    --)  54,496,747.2 ( 28.35)
    ## 4 17,789,840.3 ( 20.44) -- (    --) 178,031,019.0 ( 20.43)
    ## 5           -- (    --) -- (    --)  10,052,879.4 ( 84.38)
    ## 6    119,274.3 (100.76) -- (    --)  71,349,521.8 ( 30.27)

#### POP2: 2.4 Number of Live Trees by Species Groups and a Different Diameter Class on forest land, Bighorn National Forest

View Example

Next, we can look at number of live trees by species group and diameter class (DIACL):

    tree2.4 <- modGBtree(GBpopdat = GBpopdat.bh,             # pop - population calculations
                         landarea = "FOREST",                # est - forest land filter
                         sumunits = TRUE,                    # est - sum estimation units to population
                         estvar = "TPA_UNADJ",               # est - number of trees per acre
                         estvar.filter = "STATUSCD == 1",    # est - live trees only
                         rowvar = "SPGRPCD",                 # est - row domain
                         colvar = "DIACL",                   # est - column domain
                         returntitle = TRUE,                 # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,               # est - row domain names
                           allin1 = TRUE                     # out - return output with est(pse)
                         ))

Outputs:

    # Look at output list
    names(tree2.4)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"   "invyr"

    # Estimate and percent sampling error of estimate
    head(tree2.4$est)

output

    ##                 Species group                 0-4.9               10-19.9
    ## 1                 Douglas-fir 20,800,768.4 ( 86.59)  1,192,742.5 ( 47.37)
    ## 2                    True fir 59,505,414.9 ( 31.91)  1,798,103.0 ( 38.15)
    ## 3 Engelmann and other spruces 26,967,788.8 ( 45.56) 10,415,811.0 ( 29.66)
    ## 4              Lodgepole pine 77,371,969.0 ( 30.20) 19,823,495.2 ( 20.78)
    ## 5          Woodland softwoods           -- (    --)           -- (    --)
    ## 6     Other western softwoods  2,971,538.3 (100.76)    119,274.3 (100.76)
    ##                  20+                 5-9.9          NA                  Total
    ## 1 357,822.7 ( 74.35)  4,055,324.4 ( 51.25) -- (    --)            -- (    --)
    ## 2        -- (    --) 10,046,003.3 ( 31.06) -- (    --)  26,406,658.0 ( 72.43)
    ## 3 727,630.6 ( 71.82) 16,385,515.6 ( 32.40) -- (    --)  54,496,746.0 ( 28.35)
    ## 4 238,548.6 ( 70.35) 80,597,006.6 ( 21.14) -- (    --) 178,031,019.4 ( 20.43)
    ## 5        -- (    --)           -- (    --) -- (    --)  10,052,879.2 ( 84.38)
    ## 6        -- (    --)  1,550,565.3 ( 80.36) -- (    --)  71,349,521.2 ( 30.27)

#### POP2: 2.5 Number of Live Trees (+ seedlings) by Species Groups and a Different Diameter Class on forest land, Bighorn National Forest

View Example

Finally, we add seedlings to Example 8:

    tree2.5 <- modGBtree(GBpopdat = GBpopdat.bh,             # pop - population calculations
                         estseed = "add",                    # est - add seedling data
                         landarea = "FOREST",                # est - forest land filter
                         sumunits = TRUE,                    # est - sum estimation units to population
                         estvar = "TPA_UNADJ",               # est - number of trees per acre
                         estvar.filter = "STATUSCD == 1",    # est - live trees only
                         rowvar = "SPGRPCD",                 # est - row domain
                         colvar = "DIACL",                   # est - column domain
                         returntitle = TRUE,                 # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,               # est - row domain names
                           allin1 = TRUE                     # out - return output with est(pse)
                         ))

And look at the outputs:

    # Look at output list
    names(tree2.5)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"   "invyr"

    # Estimate and percent sampling error of estimate
    head(tree2.5$est)

output

    ##                 Species group                 0-4.9               10-19.9
    ## 1                 Douglas-fir 20,800,768.4 ( 86.59)  1,192,742.5 ( 47.37)
    ## 2                    True fir 59,505,414.9 ( 31.91)  1,798,103.0 ( 38.15)
    ## 3 Engelmann and other spruces 26,967,788.8 ( 45.56) 10,415,811.0 ( 29.66)
    ## 4              Lodgepole pine 77,371,969.0 ( 30.20) 19,823,495.2 ( 20.78)
    ## 5          Woodland softwoods           -- (    --)           -- (    --)
    ## 6     Other western softwoods  2,971,538.3 (100.76)    119,274.3 (100.76)
    ##                  20+                 5-9.9                     <1          NA
    ## 1 357,822.7 ( 74.35)  4,055,324.4 ( 51.25)  25,258,076.0 ( 88.99) -- (    --)
    ## 2        -- (    --) 10,046,003.3 ( 31.06) 339,359,745.9 ( 25.54) -- (    --)
    ## 3 727,630.6 ( 71.82) 16,385,515.6 ( 32.40)  70,428,334.4 ( 39.27) -- (    --)
    ## 4 238,548.6 ( 70.35) 80,597,006.6 ( 21.14)  72,877,337.6 ( 26.90) -- (    --)
    ## 5        -- (    --)           -- (    --)            -- (    --) -- (    --)
    ## 6        -- (    --)  1,550,565.3 ( 80.36)  34,172,691.2 ( 96.37) -- (    --)
    ##                    Total
    ## 1            -- (    --)
    ## 2  51,664,734.0 ( 80.14)
    ## 3 124,925,080.4 ( 31.82)
    ## 4 250,908,357.0 ( 19.12)
    ## 5  87,312,876.5 ( 76.24)
    ## 6 410,709,267.1 ( 24.79)

#### POP3: 3.1 Volume of Dead Trees by Forest Type Group and Primary Disturbance on forest land, Bighorn National Forest Districts

View Example

    deadtree.filter <- "STATUSCD == 2 & STANDING_DEAD_CD == 1"

    tree3.1 <- modGBtree(GBpopdat = GBpopdat.bhdist,             # pop - population calculations
                         landarea = "FOREST",                    # est - forest land filter
                         sumunits = FALSE,                       # est - sum estimation units to population
                         estvar = "VOLCFNET",                    # est - number of trees per acre
                         estvar.filter = deadtree.filter,        # est - only dead trees
                         rowvar = "FORTYPGRPCD",                 # est - row domain
                         returntitle = TRUE,                     # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,                   # est - row domain names
                           allin1 = TRUE                         # out - return output with est(pse)
                         ))

Outputs:

    # Look at output list
    names(tree3.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"   "invyr"

    # Estimate and percent sampling error of estimate
    tree3.1$est

output

    ##                       Forest-type group Medicine Wheel Ranger District
    ## 1                     Douglas-fir group             182,083.8 ( 97.68)
    ## 2 Fir / spruce / mountain hemlock group         148,461,368.7 ( 48.08)
    ## 3                  Lodgepole pine group          33,776,650.8 ( 54.79)
    ## 4                 Aspen / birch / group                    -- (    --)
    ## 5                            Nonstocked                    -- (    --)
    ## 6                                 Other                    -- (    --)
    ## 7                                 Total         182,420,103.3 ( 37.56)
    ##   Powder River Ranger District Tongue Ranger District
    ## 1                  -- (    --)            -- (    --)
    ## 2        64,537,347.3 ( 98.86)     436,728.7 (104.01)
    ## 3        11,612,957.3 ( 85.04)  28,663,666.9 ( 28.27)
    ## 4         8,948,775.3 (121.05)            -- (    --)
    ## 5                  -- (    --)   2,327,938.3 (104.01)
    ## 6                  -- (    --)            -- (    --)
    ## 7        85,099,079.9 ( 73.73)  31,428,333.9 ( 25.75)

    # Estimate and percent sampling error by district
    tree3.1$raw$unit_rowest

output

    ##                        DISTRICTNA FORTYPGRPCD        nhat      nhat.var
    ## 1  Medicine Wheel Ranger District         200   0.4995129     0.2380862
    ## 2  Medicine Wheel Ranger District         260 407.2759306 38349.8439593
    ## 3  Medicine Wheel Ranger District         280  92.6599088  2577.8844851
    ## 4    Powder River Ranger District         260 193.0327470 36420.5060121
    ## 5    Powder River Ranger District         280  34.7346328   872.6092710
    ## 6    Powder River Ranger District         900  26.7660006  1049.7838890
    ## 7          Tongue Ranger District         200   0.0000000     0.0000000
    ## 8          Tongue Ranger District         260   1.0554742     1.2050699
    ## 9          Tongue Ranger District         280  69.2735787   383.4289385
    ## 10         Tongue Ranger District         999   5.6260987    34.2398366
    ##    NBRPLT.gt0                     Forest-type group AREAUSED         est
    ## 1           1                     Douglas-fir group 364522.8    182083.8
    ## 2           5 Fir / spruce / mountain hemlock group 364522.8 148461368.7
    ## 3           5                  Lodgepole pine group 364522.8  33776650.8
    ## 4           3 Fir / spruce / mountain hemlock group 334333.7  64537347.3
    ## 5           3                  Lodgepole pine group 334333.7  11612957.3
    ## 6           1                 Aspen / birch / group 334333.7   8948775.3
    ## 7           0                     Douglas-fir group 413774.9         0.0
    ## 8           1 Fir / spruce / mountain hemlock group 413774.9    436728.7
    ## 9          13                  Lodgepole pine group 413774.9  28663666.9
    ## 10          1                            Nonstocked 413774.9   2327938.3
    ##             est.var     est.se    est.cv       pse CI99left   CI99right
    ## 1       31636151273   177865.5 0.9768332  97.68332        0    640235.1
    ## 2  5095807713173165 71384926.4 0.4808317  48.08317        0 332336753.8
    ## 3   342541254061630 18507870.1 0.5479486  54.79486        0  81449764.8
    ## 4  4071047915643802 63804764.1 0.9886487  98.86487        0 228887528.2
    ## 5    97539395879701  9876203.5 0.8504469  85.04469        0  37052371.8
    ## 6   117343798346093 10832534.3 1.2105047 121.05047        0  36851534.4
    ## 7                 0        0.0       NaN       NaN        0         0.0
    ## 8      206319592507   454224.2 1.0400602 104.00602        0   1606732.6
    ## 9    65646735561327  8102267.3 0.2826668  28.26668  7793609  49533724.4
    ## 10    5862190550058  2421196.1 1.0400602 104.00602        0   8564526.2
    ##    CI95left   CI95right     CI68left   CI68right
    ## 1         0    530693.9     5204.047    358963.6
    ## 2   8549484 288373253.4 77472065.888 219450671.4
    ## 3         0  70051409.5 15371353.487  52181948.0
    ## 4         0 189592386.9  1086196.679 127988497.9
    ## 5         0  30969960.5  1791488.901  21434425.8
    ## 6         0  30180152.3        0.000  19721274.4
    ## 7         0         0.0        0.000         0.0
    ## 8         0   1326991.7        0.000    888435.5
    ## 9  12783515  44543819.0 20606303.257  36721030.5
    ## 10        0   7073395.5        0.000   4735715.9

#### POP4: 4.1 Net cubic-foot volume of live trees by forest type group and stand-size class, Rhode Island, 2019

View Example

    tree4.1 <- modGBtree(GBpopdat = GBpopdat.RI,             # pop - population calculations
                         landarea = "FOREST",                # est - forest land filter
                         sumunits = TRUE,                    # est - sum estimation units to population
                         estvar = "VOLCFNET",                # est - net cubic-foot volume estimate
                         estvar.filter = "STATUSCD == 1",    # est - live trees only
                         rowvar = "FORTYPCD",                # est - row domain
                         colvar = "STDSZCD",                 # est - column domain
                         returntitle = TRUE,                 # out - return title information
                         table_opts = table_options(
                           row.FIAname = TRUE,               # est - row domain names
                           col.FIAname = TRUE,               # est - column domain names
                           allin1 = TRUE                     # out - return output with est(pse)
                         ))

Outputs:

    # Look at output list
    names(tree4.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "states"

    # Estimate and percent sampling error of estimate
    head(tree4.1$est)

output

    ##                                         Forest type         Large diameter
    ## 1                                Eastern white pine 111,564,015.5 ( 34.27)
    ## 2                                   Eastern hemlock   3,935,773.9 ( 95.01)
    ## 3                                        Pitch pine  29,414,660.9 ( 56.42)
    ## 4 Eastern white pine / northern red oak / white ash  44,820,940.9 ( 51.95)
    ## 5                             Other pine / hardwood   1,692,470.3 ( 94.75)
    ## 6                                      Chestnut oak            -- (    --)
    ##        Medium diameter Small diameter  Nonstocked                  Total
    ## 1          -- (    --)    -- (    --) -- (    --)   3,739,305.1 ( 84.93)
    ## 2          -- (    --)    -- (    --) -- (    --)   1,641,076.3 ( 94.71)
    ## 3          -- (    --)    -- (    --) -- (    --)   5,958,482.8 ( 96.42)
    ## 4 1,921,936.2 ( 69.09)    -- (    --) -- (    --)  79,806,857.2 ( 29.87)
    ## 5          -- (    --)    -- (    --) -- (    --)   3,935,773.9 ( 95.01)
    ## 6 5,958,482.8 ( 96.42)    -- (    --) -- (    --) 111,564,015.5 ( 34.27)

### modGBratio

`FIESTA`‘s `modGBratio` function generates per-acre and per-tree estimates based on Scott et al. 2005 (’the green-book’) for mapped forest inventory plots. The ratio estimator for estimating per-acre or per-tree by stratum and domain is used, referred to as Ratio of Means (ROM).

If there are more than one estimation unit (i.e., subpopulation) within the population, estimates are generated by estimation unit. If sumunits = TRUE, the estimates and percent standard errors returned are a sum combination of all estimation units. If rawdata = TRUE, the raw data returned will include estimates by estimation unit.

Parameters defined in the following examples are organized by category: population data (pop); estimation information (est); and output details (out).

#### POP1: 1.1 Net cubic-foot volume per acre of live trees on timberland, Wyoming, 2011-2013

View Example

We generate estimates by estimation unit (i.e., ESTN_UNIT) and sum to population (i.e., WY):

    ratio1.1 <- modGBratio(GBpopdat = GBpopdat,                # pop - population calculations
                           landarea = "TIMBERLAND",            # est - forest land filter
                           sumunits = TRUE,                    # est - sum estimation units to population
                           estvarn = "VOLCFNET",               # est - net cubic-foot volume, numerator
                           estvarn.filter = "STATUSCD == 1",   # est - live trees only, numerator
                           returntitle = TRUE)                 # out - return title information

And we can look at our output:

    # Look at output list
    names(ratio1.1)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio1.1$est)

output

    ##   TOTAL Estimate Percent Sampling Error
    ## 1 Total     1580                   4.83

    # Raw data (list object) for estimate
    raw1.1 <- ratio1.1$raw      # extract raw data list object from output
    names(raw1.1)

output

    ##  [1] "unit_totest"    "totest"         "domdat"         "estvarn"
    ##  [5] "estvarn.filter" "estvard"        "module"         "esttype"
    ##  [9] "GBmethod"       "rowvar"         "colvar"         "areaunits"
    ## [13] "estunitsn"

    head(raw1.1$unit_totest)    # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat   nhat.var       dhat     dhat.var     covar NBRPLT.gt0
    ## 1         1 180.53243  595.20944 0.16708433 0.0003208604 0.2137592         18
    ## 2        11 330.89121 3992.65230 0.24597749 0.0006412162 0.7076197         25
    ## 3        13  71.40710  336.36757 0.07062209 0.0001539055 0.1502340         21
    ## 4        17  48.43950 1749.13627 0.05603448 0.0008712607 0.7968569          3
    ## 5        19 260.38471 3487.08469 0.12740773 0.0005089103 1.0499381         18
    ## 6        21   9.51378   62.35546 0.02325581 0.0002672351 0.1093239          2
    ##   AREAUSED      estn      estd          estn.var   estn.se   estn.cv estn.pse
    ## 1  2757613 497838569 460753.93  4526228191455539  67277249 0.1351387 13.51387
    ## 2  1837124 607888182 451891.15 13475299697142006 116083159 0.1909614 19.09614
    ## 3  5930088 423450387 418795.22 11828682911925740 108759749 0.2568418 25.68418
    ## 4  1283969  62194813  71946.54  2883584768627268  53699020 0.8634003 86.34003
    ## 5  2671802 695696398 340408.24 24892644476023920 157774030 0.2267858 22.67858
    ## 6  1720074  16364406  40001.72   184488280658689  13582646 0.8300116 83.00116
    ##     estd.var  estd.se   estd.cv estd.pse     est.covar      rhat  rhat.var
    ## 1 2439960647 49395.96 0.1072068 10.72068 1625517031313 1080.4869  18192.04
    ## 2 2164120339 46520.11 0.1029454 10.29454 2388233798713 1345.2093  53701.35
    ## 3 5412232656 73567.88 0.1756655 17.56655 5283119153565 1011.1156  38076.50
    ## 4 1436339850 37899.07 0.5267672 52.67672 1313679500400  864.4587 325657.46
    ## 5 3632869614 60273.29 0.1770618 17.70618 7495010201217 2043.7120  81387.31
    ## 6  790656264 28118.61 0.7029350 70.29350  323451579355  409.0925  32601.35
    ##    rhat.se   rhat.cv      pse  CI99left CI99right   CI95left CI95right
    ## 1 134.8779 0.1248307 12.48307  733.0645 1427.9093  816.13106 1344.8427
    ## 2 231.7355 0.1722673 17.22673  748.2982 1942.1205  891.01604 1799.4026
    ## 3 195.1320 0.1929868 19.29868  508.4889 1513.7424  628.66390 1393.5673
    ## 4 570.6641 0.6601403 66.01403    0.0000 2334.3919    0.00000 1982.9397
    ## 5 285.2846 0.1395914 13.95914 1308.8675 2778.5564 1484.56442 2602.8595
    ## 6 180.5584 0.4413633 44.13633    0.0000  874.1803   55.20449  762.9806
    ##    CI68left CI68right NBRPLT
    ## 1  946.3565 1214.6172    133
    ## 2 1114.7581 1575.6605     85
    ## 3  817.0650 1205.1662    290
    ## 4  296.9574 1431.9601     58
    ## 5 1760.0084 2327.4155    128
    ## 6  229.5348  588.6503     86

    head(raw1.1$totest)         # estimates for population (i.e., WY)

output

    ##   TOTAL       estn           estn.var NBRPLT.gt0 AREAUSED    estd    estd.var
    ## 1     1 8828144235 304733266327889216        280 54457532 5587398 54138597407
    ##        est.covar    rhat rhat.var  rhat.se    rhat.cv      pse CI99left
    ## 1 81770726054087 1580.01 5813.426 76.24582 0.04825655 4.825655 1383.614
    ##   CI99right CI95left CI95right CI68left CI68right NBRPLT
    ## 1  1776.406 1430.571  1729.449 1504.187  1655.833   2638

    # Titles (list object) for estimate
    titlelst <- ratio1.1$titlelst
    names(titlelst)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.yvard"
    ##  [5] "title.unitvar" "title.ref"     "outfn.estpse"  "outfn.rawdat"
    ##  [9] "outfn.param"   "title.tot"     "title.unitsn"  "title.unitsd"

    titlelst

output

    ## $title.estpse
    ## [1] "VOLCFNET_TPA_ADJ_live per acre, in cubic feet, and percent sampling error on timberland"
    ##
    ## $title.yvar
    ## [1] ", in cubic feet"
    ##
    ## $title.estvar
    ## [1] "VOLCFNET_TPA_ADJ_live per acre"
    ##
    ## $title.yvard
    ## [1] "Acres"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] ""
    ##
    ## $outfn.estpse
    ## [1] "ratio_VOLCFNET_TPA_ADJ_live_ESTIMATED_VALUE_timberland"
    ##
    ## $outfn.rawdat
    ## [1] "ratio_VOLCFNET_TPA_ADJ_live_ESTIMATED_VALUE_timberland_rawdata"
    ##
    ## $outfn.param
    ## [1] "ratio_VOLCFNET_TPA_ADJ_live_ESTIMATED_VALUE_timberland_parameters"
    ##
    ## $title.tot
    ## [1] "VOLCFNET_TPA_ADJ_live per acre, in cubic feet, on timberland"
    ##
    ## $title.unitsn
    ## [1] "cubic feet"
    ##
    ## $title.unitsd
    ## [1] "acres"

#### POP1: 1.2 Net cubic-foot volume per acre of live trees by forest type on timberland, Wyoming, 2011-2013

View Example

    ratio1.2 <- modGBratio(GBpopdat = GBpopdat,                # pop - population calculations
                           landarea = "TIMBERLAND",            # est - forest land filter
                           sumunits = TRUE,                    # est - sum estimation units to population
                           estvarn = "VOLCFNET",               # est - net cubic-foot volume
                           estvarn.filter = "STATUSCD == 1",   # est - live trees only
                           rowvar = "FORTYPCD",                # est - row domain
                           returntitle = TRUE)                 # out - return title information

And of course view our outputs:

    # Look at output list
    names(ratio1.2)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio1.2$est)

output

    ##   Forest type Estimate Percent Sampling Error
    ## 1         182       --                     --
    ## 2         184       --                     --
    ## 3         185       --                     --
    ## 4         201   1492.1                  12.74
    ## 5         221   1275.4                  10.25
    ## 6         265   2912.6                  16.69

    # Raw data (list object) for estimate
    raw1.2 <- ratio1.2$raw      # extract raw data list object from output
    names(raw1.2)

output

    ##  [1] "unit_totest"    "totest"         "unit_rowest"    "rowest"
    ##  [5] "domdat"         "estvarn"        "estvarn.filter" "estvard"
    ##  [9] "module"         "esttype"        "GBmethod"       "rowvar"
    ## [13] "colvar"         "areaunits"      "estunitsn"

    head(raw1.2$unit_totest)    # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat   nhat.var       dhat     dhat.var     covar NBRPLT.gt0
    ## 1         1 180.53243  595.20944 0.16708433 0.0003208604 0.2137592         18
    ## 2        11 330.89121 3992.65230 0.24597749 0.0006412162 0.7076197         25
    ## 3        13  71.40710  336.36757 0.07062209 0.0001539055 0.1502340         21
    ## 4        17  48.43950 1749.13627 0.05603448 0.0008712607 0.7968569          3
    ## 5        19 260.38471 3487.08469 0.12740773 0.0005089103 1.0499381         18
    ## 6        21   9.51378   62.35546 0.02325581 0.0002672351 0.1093239          2
    ##   AREAUSED      estn      estd          estn.var   estn.se   estn.cv estn.pse
    ## 1  2757613 497838569 460753.93  4526228191455539  67277249 0.1351387 13.51387
    ## 2  1837124 607888182 451891.15 13475299697142006 116083159 0.1909614 19.09614
    ## 3  5930088 423450387 418795.22 11828682911925740 108759749 0.2568418 25.68418
    ## 4  1283969  62194813  71946.54  2883584768627268  53699020 0.8634003 86.34003
    ## 5  2671802 695696398 340408.24 24892644476023920 157774030 0.2267858 22.67858
    ## 6  1720074  16364406  40001.72   184488280658689  13582646 0.8300116 83.00116
    ##     estd.var  estd.se   estd.cv estd.pse     est.covar      rhat  rhat.var
    ## 1 2439960647 49395.96 0.1072068 10.72068 1625517031313 1080.4869  18192.04
    ## 2 2164120339 46520.11 0.1029454 10.29454 2388233798713 1345.2093  53701.35
    ## 3 5412232656 73567.88 0.1756655 17.56655 5283119153565 1011.1156  38076.50
    ## 4 1436339850 37899.07 0.5267672 52.67672 1313679500400  864.4587 325657.46
    ## 5 3632869614 60273.29 0.1770618 17.70618 7495010201217 2043.7120  81387.31
    ## 6  790656264 28118.61 0.7029350 70.29350  323451579355  409.0925  32601.35
    ##    rhat.se   rhat.cv      pse  CI99left CI99right   CI95left CI95right
    ## 1 134.8779 0.1248307 12.48307  733.0645 1427.9093  816.13106 1344.8427
    ## 2 231.7355 0.1722673 17.22673  748.2982 1942.1205  891.01604 1799.4026
    ## 3 195.1320 0.1929868 19.29868  508.4889 1513.7424  628.66390 1393.5673
    ## 4 570.6641 0.6601403 66.01403    0.0000 2334.3919    0.00000 1982.9397
    ## 5 285.2846 0.1395914 13.95914 1308.8675 2778.5564 1484.56442 2602.8595
    ## 6 180.5584 0.4413633 44.13633    0.0000  874.1803   55.20449  762.9806
    ##    CI68left CI68right NBRPLT
    ## 1  946.3565 1214.6172    133
    ## 2 1114.7581 1575.6605     85
    ## 3  817.0650 1205.1662    290
    ## 4  296.9574 1431.9601     58
    ## 5 1760.0084 2327.4155    128
    ## 6  229.5348  588.6503     86

    head(raw1.2$totest)         # estimates for population (i.e., WY)

output

    ##   TOTAL       estn           estn.var NBRPLT.gt0 AREAUSED    estd    estd.var
    ## 1     1 8828144235 304733266327889216        280 54457532 5587398 54138597407
    ##        est.covar    rhat rhat.var  rhat.se    rhat.cv      pse CI99left
    ## 1 81770726054087 1580.01 5813.426 76.24582 0.04825655 4.825655 1383.614
    ##   CI99right CI95left CI95right CI68left CI68right NBRPLT
    ## 1  1776.406 1430.571  1729.449 1504.187  1655.833   2638

    head(raw1.2$unit_rowest)    # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT Forest type      nhat  nhat.var       dhat     dhat.var      covar
    ## 1         1         201 15.992779 197.68897 0.01023188 0.0000809180 0.12647765
    ## 2         1         221 33.648351 248.18574 0.03239380 0.0002137597 0.17875130
    ## 3         1         266 66.826970 727.37825 0.04092751 0.0002629835 0.42940294
    ## 4         1         268  8.779410  59.57517 0.01023188 0.0000809180 0.06943129
    ## 5         1         281 44.163924 440.87275 0.03854903 0.0002367898 0.28843941
    ## 6         1         901  8.753505  23.80886 0.02760699 0.0002055134 0.06500960
    ##   NBRPLT.gt0 AREAUSED      estn      estd         estn.var  estn.se   estn.cv
    ## 1          1  2757613  44101894  28215.56 1503311829880000 38772565 0.8791587
    ## 2          4  2757613  92789131  89329.58 1887310973928104 43443192 0.4681927
    ## 3          4  2757613 184282921 112862.22 5531296553302686 74372687 0.4035788
    ## 4          1  2757613  24210216  28215.56  453035175150730 21284623 0.8791587
    ## 5          4  2757613 121787012 106303.32 3352585730574492 57901518 0.4754326
    ## 6          3  2757613  24138781  76129.41  181052834306616 13455587 0.5574261
    ##   estn.pse   estd.var  estd.se   estd.cv estd.pse     est.covar      rhat
    ## 1 87.91587  615335251 24805.95 0.8791587 87.91587  961790393948 1563.0348
    ## 2 46.81927 1625520387 40317.74 0.4513370 45.13370 1359301654330 1038.7280
    ## 3 40.35788 1999839565 44719.57 0.3962315 39.62315 3265364378238 1632.8132
    ## 4 87.91587  615335251 24805.95 0.8791587 87.91587  527985334199  858.0450
    ## 5 47.54326 1800651626 42434.09 0.3991793 39.91793 2193417152153 1145.6558
    ## 6 55.74261 1562811900 39532.42 0.5192792 51.92792  494360925311  317.0756
    ##       rhat.var         rhat.se          rhat.cv             pse  CI99left
    ## 1 6.280479e-10   0.00002506088 0.00000001603348  0.000001603348 1563.0347
    ## 2 1.024197e+05 320.03085289150 0.30809880361524 30.809880361524  214.3831
    ## 3 1.566717e+04 125.16858104420 0.07665823831284  7.665823831284 1310.4003
    ## 4 1.570120e-10   0.00001253044 0.00000001460348  0.000001460348  858.0450
    ## 5 6.107643e+04 247.13646317134 0.21571615561860 21.571615561860  509.0744
    ## 6 4.257232e+03  65.24746424519 0.20577886273966 20.577886273966  149.0093
    ##   CI99right  CI95left CI95right  CI68left CI68right
    ## 1  1563.035 1563.0347 1563.0348 1563.0347 1563.0348
    ## 2  1863.073  411.4791 1665.9769  720.4708 1356.9852
    ## 3  1955.226 1387.4873 1878.1391 1508.3383 1757.2881
    ## 4   858.045  858.0450  858.0450  858.0450  858.0450
    ## 5  1782.237  661.2772 1630.0344  899.8890 1391.4226
    ## 6   485.142  189.1930  444.9583  252.1898  381.9615

    head(raw1.2$rowest)         # estimates by row for population (i.e., WY)

output

    ##   Forest type       estn           estn.var NBRPLT.gt0      estd    estd.var
    ## 1         201  987424288  44159940731758880         36 661754.69 12118625799
    ## 2         221 1134539402  30684436423946804         51 889542.77 13008784420
    ## 3         265  811749127  66693410078484512         16 278701.09  5375334483
    ## 4         266 2213290055 128569040289106144         45 900929.05 15512198557
    ## 5         268 1193505511  54056169202505000         33 622891.26 10860191917
    ## 6         269   54704348   3112642041875536          1  19119.96   380241334
    ##        est.covar     rhat     rhat.var         rhat.se          rhat.cv
    ## 1 18538207847929 1492.130 3.612229e+04 190.05864411628 0.12737401825974
    ## 2 15020129516042 1275.419 1.710105e+04 130.77097083474 0.10253180408097
    ## 3 16126034158939 2912.616 2.363236e+05 486.13123082354 0.16690538688049
    ## 4 38014281366601 2456.675 4.362790e+04 208.87293877915 0.08502261094602
    ## 5 20555073694942 1916.074 3.906647e+04 197.65239246268 0.10315490585266
    ## 6  1087913214273 2861.112 2.735433e-09   0.00005230137 0.00000001828008
    ##               pse  CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1 12.737401825974 1002.5718  1981.689 1119.622  1864.638 1303.125  1681.136
    ## 2 10.253180408097  938.5749  1612.262 1019.112  1531.725 1145.372  1405.465
    ## 3 16.690538688049 1660.4245  4164.807 1959.816  3865.415 2429.179  3396.053
    ## 4  8.502261094602 1918.6542  2994.696 2047.292  2866.059 2248.960  2664.391
    ## 5 10.315490585266 1406.9548  2425.192 1528.682  2303.465 1719.517  2112.631
    ## 6  0.000001828008 2861.1123  2861.113 2861.112  2861.113 2861.112  2861.113

    # Titles (list object) for estimate
    titlelst <- ratio1.2$titlelst
    names(titlelst)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.yvard"
    ##  [5] "title.unitvar" "title.ref"     "outfn.estpse"  "outfn.rawdat"
    ##  [9] "outfn.param"   "title.rowvar"  "title.row"     "title.unitsn"
    ## [13] "title.unitsd"

    titlelst

output

    ## $title.estpse
    ## [1] "VOLCFNET_TPA_ADJ_live per acre, in cubic feet, and percent sampling error on timberland by forest type"
    ##
    ## $title.yvar
    ## [1] ", in cubic feet"
    ##
    ## $title.estvar
    ## [1] "VOLCFNET_TPA_ADJ_live per acre"
    ##
    ## $title.yvard
    ## [1] "Acres"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] ""
    ##
    ## $outfn.estpse
    ## [1] "ratio_VOLCFNET_TPA_ADJ_live_ESTIMATED_VALUE_FORTYPCD_timberland"
    ##
    ## $outfn.rawdat
    ## [1] "ratio_VOLCFNET_TPA_ADJ_live_ESTIMATED_VALUE_FORTYPCD_timberland_rawdata"
    ##
    ## $outfn.param
    ## [1] "ratio_VOLCFNET_TPA_ADJ_live_ESTIMATED_VALUE_FORTYPCD_timberland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "VOLCFNET_TPA_ADJ_live per acre, in cubic feet, on timberland by forest type"
    ##
    ## $title.unitsn
    ## [1] "cubic feet"
    ##
    ## $title.unitsd
    ## [1] "acres"

#### POP1: 1.3 Net cubic-foot volume per acre of live trees by forest type and stand-size class on timberland, Wyoming, 2011-2013

View Example

    ratio1.3 <- modGBratio(GBpopdat = GBpopdat,                # pop - population calculations
                           landarea = "TIMBERLAND",            # est - forest land filter
                           sumunits = TRUE,                    # est - sum estimation units to population
                           estvarn = "VOLCFNET",               # est - net cubic-foot volume, numerator
                           estvarn.filter = "STATUSCD == 1",   # est - live trees only, numerator
                           rowvar = "FORTYPCD",                # est - row domain
                           colvar = "STDSZCD",                 # est - column domain
                           returntitle = TRUE,                 # out - return title information
                           savedata = TRUE,                    # out - save data to outfolder
                           table_opts = table_options(
                             row.FIAname = TRUE,               # est - row domain names
                             col.FIAname = TRUE,               # est - column domain names
                             allin1 = TRUE                     # out - return output with est(pse)
                           ),
                           savedata_opts = savedata_options(
                             outfolder = outfolder,            # out - outfolder for saving data
                             outfn.pre = "WY"                  # out - prefix for output files
                           ))

And look at our output again:

    # Look at output list from modGBarea()
    names(ratio1.3)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio1.3$est)

output

    ##                 Forest type  Large diameter Medium diameter Small diameter
    ## 1    Rocky Mountain juniper      -- (   --)      -- (   --)     -- (   --)
    ## 2          Juniper woodland      -- (   --)      -- (   --)     -- (   --)
    ## 3 Pinyon / juniper woodland      -- (   --)      -- (   --)     -- (   --)
    ## 4               Douglas-fir 1,923.1 (11.46)   845.2 (20.96)  268.0 (56.76)
    ## 5            Ponderosa pine 1,366.3 (10.11) 1,125.9 (42.63)  124.2 (57.16)
    ## 6          Engelmann spruce 3,274.7 (18.15) 2,482.5 (25.35)  728.8 (25.59)
    ##   Nonstocked           Total
    ## 1 -- (   --)      -- (   --)
    ## 2 -- (   --)   622.2 (40.32)
    ## 3 -- (   --)   420.6 (   --)
    ## 4 -- (   --)   108.9 (39.10)
    ## 5 -- (   --) 2,456.7 ( 8.50)
    ## 6 -- (   --)   760.5 (26.16)

    # Raw data (list object) for estimate
    raw1.3 <- ratio1.3$raw      # extract raw data list object from output
    names(raw1.3)

output

    ##  [1] "unit_totest"    "totest"         "unit_rowest"    "rowest"
    ##  [5] "unit_colest"    "colest"         "unit_grpest"    "grpest"
    ##  [9] "domdat"         "estvarn"        "estvarn.filter" "estvard"
    ## [13] "module"         "esttype"        "GBmethod"       "rowvar"
    ## [17] "colvar"         "areaunits"      "estunitsn"

    head(raw1.3$unit_totest)    # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat   nhat.var       dhat     dhat.var     covar NBRPLT.gt0
    ## 1         1 180.53243  595.20944 0.16708433 0.0003208604 0.2137592         18
    ## 2        11 330.89121 3992.65230 0.24597749 0.0006412162 0.7076197         25
    ## 3        13  71.40710  336.36757 0.07062209 0.0001539055 0.1502340         21
    ## 4        17  48.43950 1749.13627 0.05603448 0.0008712607 0.7968569          3
    ## 5        19 260.38471 3487.08469 0.12740773 0.0005089103 1.0499381         18
    ## 6        21   9.51378   62.35546 0.02325581 0.0002672351 0.1093239          2
    ##   AREAUSED      estn      estd          estn.var   estn.se   estn.cv estn.pse
    ## 1  2757613 497838569 460753.93  4526228191455539  67277249 0.1351387 13.51387
    ## 2  1837124 607888182 451891.15 13475299697142006 116083159 0.1909614 19.09614
    ## 3  5930088 423450387 418795.22 11828682911925740 108759749 0.2568418 25.68418
    ## 4  1283969  62194813  71946.54  2883584768627268  53699020 0.8634003 86.34003
    ## 5  2671802 695696398 340408.24 24892644476023920 157774030 0.2267858 22.67858
    ## 6  1720074  16364406  40001.72   184488280658689  13582646 0.8300116 83.00116
    ##     estd.var  estd.se   estd.cv estd.pse     est.covar      rhat  rhat.var
    ## 1 2439960647 49395.96 0.1072068 10.72068 1625517031313 1080.4869  18192.04
    ## 2 2164120339 46520.11 0.1029454 10.29454 2388233798713 1345.2093  53701.35
    ## 3 5412232656 73567.88 0.1756655 17.56655 5283119153565 1011.1156  38076.50
    ## 4 1436339850 37899.07 0.5267672 52.67672 1313679500400  864.4587 325657.46
    ## 5 3632869614 60273.29 0.1770618 17.70618 7495010201217 2043.7120  81387.31
    ## 6  790656264 28118.61 0.7029350 70.29350  323451579355  409.0925  32601.35
    ##    rhat.se   rhat.cv      pse  CI99left CI99right   CI95left CI95right
    ## 1 134.8779 0.1248307 12.48307  733.0645 1427.9093  816.13106 1344.8427
    ## 2 231.7355 0.1722673 17.22673  748.2982 1942.1205  891.01604 1799.4026
    ## 3 195.1320 0.1929868 19.29868  508.4889 1513.7424  628.66390 1393.5673
    ## 4 570.6641 0.6601403 66.01403    0.0000 2334.3919    0.00000 1982.9397
    ## 5 285.2846 0.1395914 13.95914 1308.8675 2778.5564 1484.56442 2602.8595
    ## 6 180.5584 0.4413633 44.13633    0.0000  874.1803   55.20449  762.9806
    ##    CI68left CI68right NBRPLT
    ## 1  946.3565 1214.6172    133
    ## 2 1114.7581 1575.6605     85
    ## 3  817.0650 1205.1662    290
    ## 4  296.9574 1431.9601     58
    ## 5 1760.0084 2327.4155    128
    ## 6  229.5348  588.6503     86

    head(raw1.3$totest)         # estimates for population (i.e., WY)

output

    ##   TOTAL       estn           estn.var NBRPLT.gt0 AREAUSED    estd    estd.var
    ## 1     1 8828144235 304733266327889216        280 54457532 5587398 54138597407
    ##        est.covar    rhat rhat.var  rhat.se    rhat.cv      pse CI99left
    ## 1 81770726054087 1580.01 5813.426 76.24582 0.04825655 4.825655 1383.614
    ##   CI99right CI95left CI95right CI68left CI68right NBRPLT
    ## 1  1776.406 1430.571  1729.449 1504.187  1655.833   2638

    head(raw1.3$unit_rowest)    # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT FORTYPCD      nhat  nhat.var       dhat     dhat.var      covar
    ## 1         1      201 15.992779 197.68897 0.01023188 0.0000809180 0.12647765
    ## 2         1      221 33.648351 248.18574 0.03239380 0.0002137597 0.17875130
    ## 3         1      266 66.826970 727.37825 0.04092751 0.0002629835 0.42940294
    ## 4         1      268  8.779410  59.57517 0.01023188 0.0000809180 0.06943129
    ## 5         1      281 44.163924 440.87275 0.03854903 0.0002367898 0.28843941
    ## 6         1      901  8.753505  23.80886 0.02760699 0.0002055134 0.06500960
    ##   NBRPLT.gt0                      Forest type AREAUSED      estn      estd
    ## 1          1                      Douglas-fir  2757613  44101894  28215.56
    ## 2          4                   Ponderosa pine  2757613  92789131  89329.58
    ## 3          4 Engelmann spruce / subalpine fir  2757613 184282921 112862.22
    ## 4          1                    Subalpine fir  2757613  24210216  28215.56
    ## 5          4                   Lodgepole pine  2757613 121787012 106303.32
    ## 6          3                            Aspen  2757613  24138781  76129.41
    ##           estn.var  estn.se   estn.cv estn.pse   estd.var  estd.se   estd.cv
    ## 1 1503311829880000 38772565 0.8791587 87.91587  615335251 24805.95 0.8791587
    ## 2 1887310973928104 43443192 0.4681927 46.81927 1625520387 40317.74 0.4513370
    ## 3 5531296553302686 74372687 0.4035788 40.35788 1999839565 44719.57 0.3962315
    ## 4  453035175150730 21284623 0.8791587 87.91587  615335251 24805.95 0.8791587
    ## 5 3352585730574492 57901518 0.4754326 47.54326 1800651626 42434.09 0.3991793
    ## 6  181052834306616 13455587 0.5574261 55.74261 1562811900 39532.42 0.5192792
    ##   estd.pse     est.covar      rhat     rhat.var         rhat.se
    ## 1 87.91587  961790393948 1563.0348 6.280479e-10   0.00002506088
    ## 2 45.13370 1359301654330 1038.7280 1.024197e+05 320.03085289150
    ## 3 39.62315 3265364378238 1632.8132 1.566717e+04 125.16858104420
    ## 4 87.91587  527985334199  858.0450 1.570120e-10   0.00001253044
    ## 5 39.91793 2193417152153 1145.6558 6.107643e+04 247.13646317134
    ## 6 51.92792  494360925311  317.0756 4.257232e+03  65.24746424519
    ##            rhat.cv             pse  CI99left CI99right  CI95left CI95right
    ## 1 0.00000001603348  0.000001603348 1563.0347  1563.035 1563.0347 1563.0348
    ## 2 0.30809880361524 30.809880361524  214.3831  1863.073  411.4791 1665.9769
    ## 3 0.07665823831284  7.665823831284 1310.4003  1955.226 1387.4873 1878.1391
    ## 4 0.00000001460348  0.000001460348  858.0450   858.045  858.0450  858.0450
    ## 5 0.21571615561860 21.571615561860  509.0744  1782.237  661.2772 1630.0344
    ## 6 0.20577886273966 20.577886273966  149.0093   485.142  189.1930  444.9583
    ##    CI68left CI68right
    ## 1 1563.0347 1563.0348
    ## 2  720.4708 1356.9852
    ## 3 1508.3383 1757.2881
    ## 4  858.0450  858.0450
    ## 5  899.8890 1391.4226
    ## 6  252.1898  381.9615

    head(raw1.3$rowest)         # estimates by row for population (i.e., WY)

output

    ##                        Forest type       estn           estn.var NBRPLT.gt0
    ## 1                      Douglas-fir  987424288  44159940731758880         36
    ## 2                   Ponderosa pine 1134539402  30684436423946804         51
    ## 3                 Engelmann spruce  811749127  66693410078484512         16
    ## 4 Engelmann spruce / subalpine fir 2213290055 128569040289106144         45
    ## 5                    Subalpine fir 1193505511  54056169202505000         33
    ## 6                      Blue spruce   54704348   3112642041875536          1
    ##        estd    estd.var      est.covar     rhat     rhat.var         rhat.se
    ## 1 661754.69 12118625799 18538207847929 1492.130 3.612229e+04 190.05864411628
    ## 2 889542.77 13008784420 15020129516042 1275.419 1.710105e+04 130.77097083474
    ## 3 278701.09  5375334483 16126034158939 2912.616 2.363236e+05 486.13123082354
    ## 4 900929.05 15512198557 38014281366601 2456.675 4.362790e+04 208.87293877915
    ## 5 622891.26 10860191917 20555073694942 1916.074 3.906647e+04 197.65239246268
    ## 6  19119.96   380241334  1087913214273 2861.112 2.735433e-09   0.00005230137
    ##            rhat.cv             pse  CI99left CI99right CI95left CI95right
    ## 1 0.12737401825974 12.737401825974 1002.5718  1981.689 1119.622  1864.638
    ## 2 0.10253180408097 10.253180408097  938.5749  1612.262 1019.112  1531.725
    ## 3 0.16690538688049 16.690538688049 1660.4245  4164.807 1959.816  3865.415
    ## 4 0.08502261094602  8.502261094602 1918.6542  2994.696 2047.292  2866.059
    ## 5 0.10315490585266 10.315490585266 1406.9548  2425.192 1528.682  2303.465
    ## 6 0.00000001828008  0.000001828008 2861.1123  2861.113 2861.112  2861.113
    ##   CI68left CI68right
    ## 1 1303.125  1681.136
    ## 2 1145.372  1405.465
    ## 3 2429.179  3396.053
    ## 4 2248.960  2664.391
    ## 5 1719.517  2112.631
    ## 6 2861.112  2861.113

    head(raw1.3$unit_colest)    # estimates by column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT STDSZCD       nhat    nhat.var        dhat      dhat.var      covar
    ## 1         1       1  87.798754  752.714537 0.063089433 0.00035128489 0.44024129
    ## 2         1       2  81.400543  618.813946 0.061391258 0.00033378677 0.44257806
    ## 3         1       3   8.965642   23.683588 0.035460400 0.00023765695 0.06312483
    ## 4         1       5   2.367487    5.908853 0.007143242 0.00005379211 0.01782834
    ## 5        11       1 297.218677 4439.407623 0.173224615 0.00079375869 1.44235746
    ## 6        11       2  25.307742  335.636402 0.028134413 0.00030048790 0.20361083
    ##   NBRPLT.gt0 Stand-size class AREAUSED      estn      estd          estn.var
    ## 1          7   Large diameter  2757613 242114986 173976.24  5723964600699634
    ## 2          6  Medium diameter  2757613 224471197 169293.33  4705726997342543
    ## 3          4   Small diameter  2757613  24723772  97786.06   180100173044904
    ## 4          1       Nonstocked  2757613   6528614  19698.30    44933452745364
    ## 5         18   Large diameter  1837124 546027565 318235.10 14983109899193358
    ## 6          3  Medium diameter  1837124  46493459  51686.41  1132781111943495
    ##     estn.se   estn.cv  estn.pse   estd.var  estd.se   estd.cv  estd.pse
    ## 1  75656887 0.3124833  31.24833 2671321181 51684.83 0.2970798  29.70798
    ## 2  68598302 0.3055996  30.55996 2538257910 50381.13 0.2975966  29.75966
    ## 3  13420141 0.5428031  54.28031 1807245526 42511.71 0.4347420  43.47420
    ## 4   6703242 1.0267481 102.67481  409058305 20225.19 1.0267481 102.67481
    ## 5 122405514 0.2241746  22.41746 2678955113 51758.62 0.1626427  16.26427
    ## 6  33656814 0.7239043  72.39043 1014154052 31845.79 0.6161347  61.61347
    ##       est.covar      rhat   rhat.var   rhat.se    rhat.cv       pse   CI99left
    ## 1 3347783858597 1391.6555  52187.659 228.44618 0.16415427 16.415427  803.21708
    ## 2 3365553643915 1325.9305   8486.906  92.12440 0.06947906  6.947906 1088.63379
    ## 3  480028290212  252.8353   5531.559  74.37445 0.29416160 29.416160   61.25946
    ## 4  135574341305  331.4304      0.000   0.00000 0.00000000  0.000000  331.43036
    ## 5 4867991897848 1715.7993  60873.448 246.72545 0.14379622 14.379622 1080.27668
    ## 6  687191544561  899.5298 268423.500 518.09603 0.57596320 57.596320    0.00000
    ##   CI99right  CI95left CI95right  CI68left CI68right
    ## 1 1980.0938  943.9092 1839.4017 1164.4754 1618.8356
    ## 2 1563.2273 1145.3700 1506.4910 1234.3167 1417.5444
    ## 3  444.4112  107.0641  398.6066  178.8731  326.7976
    ## 4  331.4304  331.4304  331.4304  331.4304  331.4304
    ## 5 2351.3220 1232.2263 2199.3723 1470.4413 1961.1574
    ## 6 2234.0567    0.0000 1914.9793  384.3051 1414.7544

    head(raw1.3$colest)         # estimates by column for population (i.e., WY)

output

    ##   Stand-size class       estn           estn.var NBRPLT.gt0      estd
    ## 1   Large diameter 6342911970 284614025898163968        161 2857890.7
    ## 2  Medium diameter 2048714141  72812251534163824         71 1345938.0
    ## 3   Small diameter  405193762   6047803810047158         51 1095815.5
    ## 4       Nonstocked   31324363    218001721639861         10  287753.9
    ##      estd.var      est.covar      rhat  rhat.var   rhat.se    rhat.cv       pse
    ## 1 38575741797 84989026109952 2219.4383 11922.651 109.19089 0.04919754  4.919754
    ## 2 22537912872 34784990438813 1522.1460 10562.957 102.77625 0.06752062  6.752062
    ## 3 19643622069  7399220151095  369.7646  2716.209  52.11726 0.14094715 14.094715
    ## 4  5235434732   597170236633  108.8582  1811.887  42.56626 0.39102502 39.102502
    ##    CI99left CI99right   CI95left CI95right  CI68left CI68right
    ## 1 1938.1812 2500.6954 2005.42806 2433.4485 2110.8525 2328.0240
    ## 2 1257.4120 1786.8801 1320.70829 1723.5838 1419.9394 1624.3527
    ## 3  235.5194  504.0097  267.61661  471.9125  317.9361  421.5930
    ## 4    0.0000  218.5016   25.42981  192.2865   66.5278  151.1885

    head(raw1.3$unit_grpest)    # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT FORTYPCD STDSZCD     nhat  nhat.var       dhat     dhat.var
    ## 1         1      201       1 15.99278 197.68897 0.01023188 0.0000809180
    ## 2         1      221       1 33.64835 248.18574 0.03239380 0.0002137597
    ## 3         1      266       1 38.15762 532.23016 0.02046375 0.0001517213
    ## 4         1      266       2 28.66935 300.84047 0.02046375 0.0001517213
    ## 5         1      268       2  8.77941  59.57517 0.01023188 0.0000809180
    ## 6         1      281       2 43.95179 441.73879 0.03069563 0.0002124098
    ##        covar NBRPLT.gt0 Stand-size class                      Forest type
    ## 1 0.12647765          1   Large diameter                      Douglas-fir
    ## 2 0.17875130          4   Large diameter                   Ponderosa pine
    ## 3 0.28290621          2   Large diameter Engelmann spruce / subalpine fir
    ## 4 0.21255872          2  Medium diameter Engelmann spruce / subalpine fir
    ## 5 0.06943129          1  Medium diameter                    Subalpine fir
    ## 6 0.30414065          3  Medium diameter                   Lodgepole pine
    ##   AREAUSED      estn     estd         estn.var  estn.se   estn.cv estn.pse
    ## 1  2757613  44101894 28215.56 1503311829880000 38772565 0.8791587 87.91587
    ## 2  2757613  92789131 89329.58 1887310973928104 43443192 0.4681927 46.81927
    ## 3  2757613 105223961 56431.11 4047306738857966 63618447 0.6046004 60.46004
    ## 4  2757613  79058960 56431.11 2287720127679708 47830117 0.6049930 60.49930
    ## 5  2757613  24210216 28215.56  453035175150730 21284623 0.8791587 87.91587
    ## 6  2757613 121202020 84646.67 3359171440814228 57958360 0.4781963 47.81963
    ##     estd.var  estd.se   estd.cv estd.pse     est.covar     rhat     rhat.var
    ## 1  615335251 24805.95 0.8791587 87.91587  961790393948 1563.035 6.280479e-10
    ## 2 1625520387 40317.74 0.4513370 45.13370 1359301654330 1038.728 1.024197e+05
    ## 3 1153753595 33966.95 0.6019188 60.19188 2151340312923 1864.645 1.124891e+04
    ## 4 1153753595 33966.95 0.6019188 60.19188 1616387815813 1400.982 7.282272e+03
    ## 5  615335251 24805.95 0.8791587 87.91587  527985334199  858.045 1.570120e-10
    ## 6 1615255033 40190.24 0.4748000 47.48000 2312816114921 1431.858 6.635926e+03
    ##           rhat.se          rhat.cv             pse  CI99left CI99right
    ## 1   0.00002506088 0.00000001603348  0.000001603348 1563.0347  1563.035
    ## 2 320.03085289150 0.30809880361524 30.809880361524  214.3831  1863.073
    ## 3 106.06088685669 0.05687995004509  5.687995004509 1591.4498  2137.839
    ## 4  85.33622796395 0.06091173099517  6.091173099517 1181.1703  1620.793
    ## 5   0.00001253044 0.00000001460348  0.000001460348  858.0450   858.045
    ## 6  81.46119115683 0.05689194147033  5.689194147033 1222.0280  1641.688
    ##    CI95left CI95right  CI68left CI68right
    ## 1 1563.0347  1563.035 1563.0347  1563.035
    ## 2  411.4791  1665.977  720.4708  1356.985
    ## 3 1656.7690  2072.520 1759.1714  1970.118
    ## 4 1233.7259  1568.238 1316.1185  1485.845
    ## 5  858.0450   858.045  858.0450   858.045
    ## 6 1272.1972  1591.519 1350.8484  1512.868

    head(raw1.3$grpest)         # estimates by row and column for population (i.e., WY)

output

    ##      Forest type Stand-size class       estn          estn.var NBRPLT.gt0
    ## 1    Douglas-fir   Large diameter  866420241 42055257694861488         26
    ## 2    Douglas-fir  Medium diameter   94302693  1884136858524552          6
    ## 3    Douglas-fir   Small diameter   26701354   381776969498459          6
    ## 4 Ponderosa pine   Large diameter 1074972177 30722964357891884         46
    ## 5 Ponderosa pine  Medium diameter   52603087  1229672235522194          3
    ## 6 Ponderosa pine   Small diameter    6964138    34976039086030          3
    ##        estd    estd.var      est.covar      rhat   rhat.var   rhat.se   rhat.cv
    ## 1 450541.00  8124659016 16185152518284 1923.0664  48532.613 220.30119 0.1145572
    ## 2 111576.95  2129966306  1783582849347  845.1808  31385.970 177.16086 0.2096130
    ## 3  99636.75  1792776867   523927717480  267.9870  23139.530 152.11683 0.5676276
    ## 4 786751.82 11316613869 14648027900685 1366.3422  19098.261 138.19646 0.1011434
    ## 5  46720.27   799177795   772616673156 1125.9158 230428.920 480.03012 0.4263464
    ## 6  56070.68  1071721490   143558035522  124.2028   5040.888  70.99921 0.5716392
    ##        pse  CI99left CI99right  CI95left CI95right   CI68left CI68right
    ## 1 11.45572 1355.6081 2490.5246 1491.2840 2354.8488 1703.98612 2142.1466
    ## 2 20.96130  388.8447 1301.5169  497.9519 1192.4097  669.00177 1021.3598
    ## 3 56.76276    0.0000  659.8140    0.0000  566.1305  116.71323  419.2608
    ## 4 10.11434 1010.3717 1722.3126 1095.4821 1637.2022 1228.91159 1503.7727
    ## 5 42.63464    0.0000 2362.3915  185.0741 2066.7576  648.54607 1603.2855
    ## 6 57.16392    0.0000  307.0847    0.0000  263.3587   53.59711  194.8086

    # Titles (list object) for estimate
    titlelst <- ratio1.3$titlelst
    names(titlelst)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.yvard"
    ##  [5] "title.unitvar" "title.ref"     "outfn.estpse"  "outfn.rawdat"
    ##  [9] "outfn.param"   "title.rowvar"  "title.row"     "title.colvar"
    ## [13] "title.col"     "title.unitsn"  "title.unitsd"

    titlelst

output

    ## $title.estpse
    ## [1] "VOLCFNET_TPA_ADJ_live per acre, in cubic feet (percent sampling error), by forest type and stand-size class on timberland"
    ##
    ## $title.yvar
    ## [1] ", in cubic feet"
    ##
    ## $title.estvar
    ## [1] "VOLCFNET_TPA_ADJ_live per acre"
    ##
    ## $title.yvard
    ## [1] "Acres"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] ""
    ##
    ## $outfn.estpse
    ## [1] "WY_ratio_VOLCFNET_TPA_ADJ_live_ESTIMATED_VALUE_FORTYPCD_STDSZCD_timberland"
    ##
    ## $outfn.rawdat
    ## [1] "WY_ratio_VOLCFNET_TPA_ADJ_live_ESTIMATED_VALUE_FORTYPCD_STDSZCD_timberland_rawdata"
    ##
    ## $outfn.param
    ## [1] "WY_ratio_VOLCFNET_TPA_ADJ_live_ESTIMATED_VALUE_FORTYPCD_STDSZCD_timberland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "VOLCFNET_TPA_ADJ_live per acre, in cubic feet (percent sampling error), by forest type on timberland"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "VOLCFNET_TPA_ADJ_live per acre, in cubic feet (percent sampling error), by stand-size class on timberland"
    ##
    ## $title.unitsn
    ## [1] "cubic feet"
    ##
    ## $title.unitsd
    ## [1] "acres"

#### POP2: 2.1 Number of live trees per acre by species, Bighorn National Forest

View Example

    ratio2.1 <- modGBratio(GBpopdat = GBpopdat.bh,              # pop - population calculations
                           landarea = "FOREST",                 # est - forest land filter
                           sumunits = TRUE,                     # est - sum estimation units to population
                           estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
                           estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
                           rowvar = "SPCD",                     # est - row domain
                           returntitle = TRUE,                  # out - return title information
                           table_opts = table_options(
                             row.FIAname = TRUE,                # est - row domain names
                             allin1 = FALSE,                    # out - return output with est and pse
                           ),
                           title_opts = title_options(
                             title.ref = "Bighorn National Forest"
                           ))

And we can look at our output:

    # Look at output list
    names(ratio2.1)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio2.1$est)

output

    ##                  Species Estimate Percent Sampling Error
    ## 1          subalpine fir      114                  28.76
    ## 2 Rocky Mountain juniper       --                     --
    ## 3       Engelmann spruce     87.1                  26.54
    ## 4         lodgepole pine    284.5                  17.82
    ## 5            limber pine      7.4                  89.98
    ## 6            Douglas-fir     42.2                  71.81

#### POP2: 2.2 Number of live trees (including seedlings) per acre by species, Bighorn National Forest

View Example

    ratio2.2 <- modGBratio(GBpopdat = GBpopdat.bh,              # pop - population calculations
                           estseed = "add",                     # est - add seedling data
                           landarea = "FOREST",                 # est - forest land filter
                           sumunits = TRUE,                     # est - sum estimation units to population
                           estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
                           estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
                           rowvar = "SPCD",                     # est - row domain
                           returntitle = TRUE,                  # out - return title information
                           table_opts = table_options(
                             row.FIAname = TRUE,                # est - row domain names
                             allin1 = FALSE                     # out - return output with est and pse
                           ),
                           title_opts = title_options(
                             title.ref = "Bighorn National Forest"
                           ))

Output and comparison:

    # Look at output list
    names(ratio2.2)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio2.2$est)

output

    ##                  Species Estimate Percent Sampling Error
    ## 1          subalpine fir    635.7                  22.36
    ## 2 Rocky Mountain juniper       --                     --
    ## 3       Engelmann spruce    193.3                  30.43
    ## 4         lodgepole pine    388.3                  17.07
    ## 5            limber pine     60.1                  95.14
    ## 6            Douglas-fir       80                  79.59

    # Compare estimates with and without seedlings
    head(ratio2.1$est)

output

    ##                  Species Estimate Percent Sampling Error
    ## 1          subalpine fir      114                  28.76
    ## 2 Rocky Mountain juniper       --                     --
    ## 3       Engelmann spruce     87.1                  26.54
    ## 4         lodgepole pine    284.5                  17.82
    ## 5            limber pine      7.4                  89.98
    ## 6            Douglas-fir     42.2                  71.81

    head(ratio2.2$est)

output

    ##                  Species Estimate Percent Sampling Error
    ## 1          subalpine fir    635.7                  22.36
    ## 2 Rocky Mountain juniper       --                     --
    ## 3       Engelmann spruce    193.3                  30.43
    ## 4         lodgepole pine    388.3                  17.07
    ## 5            limber pine     60.1                  95.14
    ## 6            Douglas-fir       80                  79.59

#### POP2: 2.3 Number of live seedlings per acre by species, Bighorn National Forest

View Example

    ratio2.3 <- modGBratio(GBpopdat = GBpopdat.bh,         # pop - population calculations
                           estseed = "only",               # est - add seedling data
                           landarea = "FOREST",            # est - forest land filter
                           sumunits = TRUE,                # est - sum estimation units to population
                           estvarn = "TPA_UNADJ",          # est - number of trees per acre, numerator
                           rowvar = "SPCD",                # est - row domain
                           returntitle = TRUE,             # out - return title information
                           table_opts = table_options(
                             row.FIAname = TRUE,           # est - row domain names
                             allin1 = FALSE                # out - return output with est and pse
                           ))

Output and comparisons:

    # Look at output list
    names(ratio2.3)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio2.3$est)

output

    ##                                Species Estimate Percent Sampling Error
    ## 1     subalpine fir (Abies lasiocarpa)    626.6                  21.69
    ## 2 Engelmann spruce (Picea engelmannii)      130                  37.42
    ## 3      lodgepole pine (Pinus contorta)    134.6                  25.37
    ## 4         limber pine (Pinus flexilis)     63.1                  95.58
    ## 5  Douglas-fir (Pseudotsuga menziesii)     46.6                  88.14
    ## 6  quaking aspen (Populus tremuloides)    142.7                  83.11

    # Compare estimates with, without, and only seedlings
    head(ratio2.1$est)

output

    ##                  Species Estimate Percent Sampling Error
    ## 1          subalpine fir      114                  28.76
    ## 2 Rocky Mountain juniper       --                     --
    ## 3       Engelmann spruce     87.1                  26.54
    ## 4         lodgepole pine    284.5                  17.82
    ## 5            limber pine      7.4                  89.98
    ## 6            Douglas-fir     42.2                  71.81

    head(ratio2.2$est)

output

    ##                  Species Estimate Percent Sampling Error
    ## 1          subalpine fir    635.7                  22.36
    ## 2 Rocky Mountain juniper       --                     --
    ## 3       Engelmann spruce    193.3                  30.43
    ## 4         lodgepole pine    388.3                  17.07
    ## 5            limber pine     60.1                  95.14
    ## 6            Douglas-fir       80                  79.59

    head(ratio2.3$est)

output

    ##                                Species Estimate Percent Sampling Error
    ## 1     subalpine fir (Abies lasiocarpa)    626.6                  21.69
    ## 2 Engelmann spruce (Picea engelmannii)      130                  37.42
    ## 3      lodgepole pine (Pinus contorta)    134.6                  25.37
    ## 4         limber pine (Pinus flexilis)     63.1                  95.58
    ## 5  Douglas-fir (Pseudotsuga menziesii)     46.6                  88.14
    ## 6  quaking aspen (Populus tremuloides)    142.7                  83.11

#### POP2: 2.4 Number of live trees by forest type and species, Bighorn National Forest

View Example

    ratio2.4 <- modGBratio(GBpopdat = GBpopdat.bh,              # pop - population calculations
                           landarea = "FOREST",                 # est - forest land filter
                           sumunits = TRUE,                     # est - sum estimation units to population
                           estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
                           estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
                           rowvar = "FORTYPCD",                 # est - row domain
                           colvar = "SPCD",                     # est - column domain
                           returntitle = TRUE,                  # out - return title information
                           table_opts = table_options(
                             row.FIAname = TRUE,                # est - row domain names
                             col.FIAname = TRUE,                # est - column domain names
                             allin1 = TRUE                      # out - return output with est(pse)
                           ))

And view our output:

    # Look at output list
    names(ratio2.4)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio2.4$est)

output

    ##                        Forest type  subalpine fir Rocky Mountain juniper
    ## 1                      Douglas-fir    -- (    --)            -- (    --)
    ## 2                 Engelmann spruce  88.6 ( 62.90)            -- (    --)
    ## 3 Engelmann spruce / subalpine fir 341.3 ( 43.08)            -- (    --)
    ## 4                    Subalpine fir 282.1 ( 49.88)            -- (    --)
    ## 5                   Lodgepole pine  64.8 ( 33.04)            -- (    --)
    ## 6                            Aspen    -- (    --)            -- (    --)
    ##   Engelmann spruce lodgepole pine    limber pine    Douglas-fir quaking aspen
    ## 1      -- (    --)    -- (    --) 108.1 ( 68.11) 535.4 ( 54.74)   -- (    --)
    ## 2   209.8 ( 22.86)  71.5 ( 81.44)    -- (    --)  54.1 ( 83.85)   -- (    --)
    ## 3   153.2 ( 50.46)    -- (    --)   4.5 ( 86.48)  18.1 ( 65.02)   -- (    --)
    ## 4     3.0 ( 72.13)  63.2 ( 37.78)    -- (    --)    -- (    --)   -- (    --)
    ## 5    67.8 ( 41.01) 376.6 ( 16.07)    -- (    --)   0.6 (100.71) 23.8 ( 82.87)
    ## 6      -- (    --) 599.7 (    --)    -- (    --)    -- (    --)   -- (    --)
    ##            Total
    ## 1 599.7 (    --)
    ## 2 643.5 ( 56.98)
    ## 3 424.0 ( 12.33)
    ## 4 590.9 ( 26.06)
    ## 5 553.1 ( 10.85)
    ## 6    -- (    --)

#### POP2: 2.5 Number of standing dead trees by species and cause of death, Bighorn National Forest

View Example

Next, we look at dead trees:

    deadtree.filter <- "STATUSCD == 2 & STANDING_DEAD_CD == 1"

    ratio2.5 <- modGBratio(GBpopdat = GBpopdat.bh,              # pop - population calculations
                           landarea = "FOREST",                 # est - forest land filter
                           sumunits = TRUE,                     # est - sum estimation units to population
                           estvarn = "VOLCFNET",                # est - number of trees per acre, numerator
                           estvarn.filter = deadtree.filter,    # est - standing dead trees only, numerator
                           rowvar = "SPCD",                     # est - row domain
                           colvar = "AGENTCD",                  # est - column domain
                           returntitle = TRUE,                  # out - return title information
                           table_opts = table_options(
                             row.FIAname = TRUE,                # est - row domain names
                             col.FIAname = TRUE,                # est - column domain names
                             allin1 = TRUE                      # out - return output with est(pse)
                           ))

And we can see our output:

    # Look at output list
    names(ratio2.5)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio2.5$est)

output

    ##                  Species        Insect      Disease         Fire
    ## 1          subalpine fir  6.8 ( 99.50) 65.1 (60.03)  6.0 (98.84)
    ## 2 Rocky Mountain juniper    -- (   --)   -- (   --)   -- (   --)
    ## 3       Engelmann spruce 108.3 (96.33)  8.7 (73.25) 67.9 (98.84)
    ## 4         lodgepole pine   8.7 (61.71) 3.0 ( 99.50)   -- (   --)
    ## 5            limber pine    -- (   --)   -- (   --) 5.4 ( 99.50)
    ## 6            Douglas-fir    -- (   --)   -- (   --)   -- (   --)
    ##   Unidentified animal      Weather Vegetation (e.g., suppression) Unidentified
    ## 1          -- (   --)  1.3 (98.84)                   0.7 ( 99.50) 21.1 (63.96)
    ## 2          -- (   --)   -- (   --)                     -- (   --)   -- (   --)
    ## 3          -- (   --) 2.1 ( 99.50)                     -- (   --) 16.0 (77.09)
    ## 4         6.3 (81.53)  2.1 (75.71)                     -- (   --) 28.3 (52.69)
    ## 5          -- (   --)   -- (   --)                     -- (   --) 0.3 ( 99.50)
    ## 6          -- (   --)   -- (   --)                     -- (   --)   -- (   --)
    ##        Other         Total
    ## 1 -- (   --)    -- (   --)
    ## 2 -- (   --)  48.4 (39.67)
    ## 3 -- (   --) 203.1 (60.69)
    ## 4 -- (   --)    -- (   --)
    ## 5 -- (   --)   5.7 (93.77)
    ## 6 -- (   --)    -- (   --)

#### POP3.1: 3.1 Number of live trees by species group and diameter class (DIACL2IN), Bighorn National Forest

View Example

    ratio2.6 <- modGBratio(GBpopdat = GBpopdat.bh,              # pop - population calculations
                           landarea = "FOREST",                 # est - forest land filter
                           sumunits = TRUE,                     # est - sum estimation units to population
                           estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
                           estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
                           rowvar = "SPGRPCD",                  # est - row domain
                           colvar = "DIACL2IN",                 # est - column domain
                           returntitle = TRUE,                  # out - return title information
                           table_opts = list(
                             row.FIAname = TRUE,                # est - row domain names
                             allin1 = TRUE                      # out - return output with est(pse)
                           ),
                           title_opts = list(
                             title.ref = "Bighorn National Forest"
                           ))

And examine our output:

    # Look at output list
    names(ratio2.6)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio2.6$est)

output

    ##                 Species group       1.0-2.9     11.0-12.9    13.0-14.9
    ## 1                 Douglas-fir 23.7 (100.28)  0.2 (100.28) 0.6 ( 73.70)
    ## 2                    True fir 57.1 ( 32.63)  2.1 ( 42.54) 0.2 (101.91)
    ## 3 Engelmann and other spruces 31.2 ( 50.66)  6.5 ( 26.66) 3.3 ( 36.94)
    ## 4              Lodgepole pine 68.9 ( 35.20) 11.5 ( 20.22) 5.2 ( 28.95)
    ## 5          Woodland softwoods   -- (    --)   -- (    --)  -- (    --)
    ## 6     Other western softwoods  4.7 (100.28)   -- (    --)  -- (    --)
    ##      15.0-16.9    17.0-18.9    19.0-20.9    21.0-22.9    23.0-24.9   25.0-26.9
    ## 1 0.6 (100.28)  -- (    --) 0.2 (100.28) 0.2 (100.28) 0.2 (100.28) -- (    --)
    ## 2 0.2 ( 98.87) 0.2 (100.28)  -- (    --)  -- (    --)  -- (    --) -- (    --)
    ## 3 1.3 ( 64.11) 1.5 ( 53.86) 1.7 ( 40.80) 0.2 (100.28) 0.4 ( 98.87) -- (    --)
    ## 4 1.7 ( 40.25) 1.3 ( 73.38) 0.6 ( 74.44)  -- (    --) 0.2 (100.28) -- (    --)
    ## 5  -- (    --)  -- (    --)  -- (    --)  -- (    --)  -- (    --) -- (    --)
    ## 6  -- (    --)  -- (    --)  -- (    --)  -- (    --)  -- (    --) -- (    --)
    ##     27.0-28.9       3.0-4.9       5.0-6.9       7.0-8.9      9.0-10.9
    ## 1 -- (    --)  9.5 ( 59.61)  3.4 ( 45.83)  2.5 ( 59.84)  1.1 ( 56.58)
    ## 2 -- (    --) 38.0 ( 40.28) 10.3 ( 30.67)  4.6 ( 35.29)  1.3 ( 33.44)
    ## 3 -- (    --) 11.9 ( 59.40) 13.2 ( 28.62) 10.3 ( 33.31)  5.6 ( 34.83)
    ## 4 -- (    --) 54.8 ( 32.53) 59.6 ( 22.03) 52.4 ( 19.20) 28.4 ( 18.15)
    ## 5 -- (    --)   -- (    --)   -- (    --)   -- (    --)   -- (    --)
    ## 6 -- (    --)   -- (    --)  1.9 ( 82.02)  0.6 ( 73.70)  0.2 (100.28)
    ##            NA          Total
    ## 1 -- (    --)    -- (    --)
    ## 2 -- (    --)  42.2 ( 71.81)
    ## 3 -- (    --)  87.1 ( 26.54)
    ## 4 -- (    --) 284.5 ( 17.82)
    ## 5 -- (    --)  16.1 ( 83.81)
    ## 6 -- (    --) 114.0 ( 28.76)

#### POP3: 3.2 Number of Live Trees per acre by Species Group and Diameter Class, Bighorn National Forest Districts

View Example

    ratio3.2 <- modGBratio(GBpopdat = GBpopdat.bhdist,          # pop - population calculations
                           landarea = "FOREST",                 # est - forest land filter
                           sumunits = TRUE,                     # est - sum estimation units to population
                           estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
                           estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
                           rowvar = "SPGRPCD",                  # est - row domain
                           returntitle = TRUE,                  # out - return title information
                           table_opts = table_options(
                             row.FIAname = TRUE,                # est - row domain names
                             allin1 = TRUE,                     # out - return output with est(pse)
                           ),
                           title_opts = title_options(
                             title.ref="Bighorn National Forest Districts"
                           ))

And of course examine our output:

    # Look at output list
    names(ratio3.2)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio3.2$est)

output

    ##                 Species group Estimate (% Sampling Error)
    ## 1          Woodland softwoods                  -- (   --)
    ## 2                 Douglas-fir                49.2 (74.23)
    ## 3 Engelmann and other spruces                84.4 (29.47)
    ## 4              Lodgepole pine               277.4 (18.48)
    ## 5        Cottonwood and aspen                14.4 (99.09)
    ## 6                    True fir               116.9 (30.70)

#### POP1: 1.4 Net cubic-foot volume of live trees (at least 5 inches diameter) divided by net cubic-foot volume of all trees by forest type, Wyoming, 2011-2013

View Example

    ratio1.4 <- modGBratio(GBpopdat = GBpopdat,                 # pop - population calculations for WY, post-stratification
                           landarea = "FOREST",                 # est - forest land filter
                           sumunits = TRUE,                     # est - sum estimation units to population
                           estvarn = "VOLCFNET",                # est - net cubic-foot volume, numerator
                           estvarn.filter = "STATUSCD == 1",    # est - live trees only
                           estvard = "VOLCFNET",                # est - net cubic-foot volume, numerator
                           rowvar = "FORTYPCD",                 # est - row domain
                           returntitle = TRUE,                  # out - return title information
                           table_opts = table_options(
                             row.FIAname = TRUE,                # est - row domain names
                             allin1 = TRUE,                     # out - return output with est(pse)
                           ))

And examine the output:

    # Look at output list
    names(ratio1.4)

output

    ## [1] "est"      "titlelst" "raw"

    # Estimate and percent sampling error of estimate
    head(ratio1.4$est)

output

    ##                Forest type Estimate (% Sampling Error)
    ## 1                    Aspen               681.5 (22.93)
    ## 2              Blue spruce             2,861.1 (   --)
    ## 3                  Bur oak               345.3 (44.36)
    ## 4               Cottonwood             1,926.8 (34.50)
    ## 5              Douglas-fir             1,451.9 (13.70)
    ## 6 Elm / ash / black locust               133.7 (   --)

### No strata

If you want to exclude post-stratification (i.e., simple random sample, Horvitz-Thompson), you must generate a new population dataset using the `modGBpop` function by setting `strata = FALSE`.

View Code

    ## Get population data for Wyoming estimates, with post-stratification (for comparison)
    GBpopdat.strat <- modGBpop(popTabs = popTables(
                                           cond = WYcond,               # FIA plot/condition data
                                           tree = WYtree,               # FIA tree data
                                           seed = WYseed                # FIA seedling data
                                         ),
                               pltassgn = WYpltassgn,                   # plot assignments
                               pltassgnid = "CN",                       # uniqueid of plots
                               unitarea = WYunitarea,                   # area by estimation units
                               unitvar = "ESTN_UNIT",                   # name of estimation unit
                               strata = TRUE,                           # if using post-stratification
                               stratalut = WYstratalut,                 # strata classes and pixels counts
                               strata_opts = strata_options(
                                 getwt=TRUE,                            # calculate strata weights
                                 getwtvar="P1POINTCNT"                  # use P1POINTCNT in stratalut to calculate weights
                               ))

    ## Get population data for Wyoming estimates, with no post-stratification
    GBpopdat.nostrat <- modGBpop(popTabs = popTables(
                                              cond = WYcond,            # FIA plot/condition data
                                              tree = WYtree,            # FIA tree data
                                              seed = WYseed             # FIA seedling data
                                           ),
                                 pltassgn = WYpltassgn,                 # plot assignments
                                 pltassgnid = "CN",                     # uniqueid of plots
                                 unitarea = WYunitarea,                 # area by estimation units
                                 unitvar = "ESTN_UNIT",                 # name of estimation unit
                                 strata = FALSE)                        # if using post-stratification

    ## Area of forest land by forest type and stand-size class, Wyoming, 2011-2013, with post-stratification
    area.strat <- modGBarea(GBpopdat = GBpopdat.strat,        # pop - population calculations for WY, post-stratification
                            landarea = "FOREST",              # est - forest land filter
                            sumunits = TRUE,                  # est - sum estimation units to population
                            rowvar = "FORTYPCD",              # est - row domain
                            table_opts = table_options(
                              row.FIAname = TRUE,             # est - row domain names
                              allin1 = FALSE                  # out - return output with est(pse)
                            ))

    ## Area of forest land by forest type and stand-size class, Wyoming, 2011-2013, no post-stratification
    area.nostrat <- modGBarea(GBpopdat = GBpopdat.nostrat,    # pop - population calculations for WY, no post-stratification
                              landarea = "FOREST",            # est - forest land filter
                              sumunits = TRUE,                # est - sum estimation units to population
                              rowvar = "FORTYPCD",            # est - row domain
                              table_opts = table_options(
                                row.FIAname = TRUE,           # est - row domain names
                                allin1 = FALSE                # out - return output with est(pse)
                              ))

    # Compare estimates and percent standard errors with and without post-stratification
    head(area.strat$est)

output

    ##                 Forest type Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper 632481.7                  17.28
    ## 2          Juniper woodland 339749.8                  23.85
    ## 3 Pinyon / juniper woodland  14854.7                    100
    ## 4               Douglas-fir   881189                  14.21
    ## 5            Ponderosa pine 889542.8                  12.82
    ## 6          Engelmann spruce 467196.7                  19.99

    head(area.nostrat$est)

output

    ##                 Forest type Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper 637719.7                  17.17
    ## 2          Juniper woodland 340093.9                   23.8
    ## 3 Pinyon / juniper woodland  14854.7                    100
    ## 4               Douglas-fir 863774.3                  14.62
    ## 5            Ponderosa pine 944562.8                  13.77
    ## 6          Engelmann spruce 473242.2                  20.02

    ## Number of live trees by species, Wyoming, 2011-2013, with post-stratification
    tree.strat <- modGBtree(GBpopdat = GBpopdat.strat,          # pop - population calculations for WY, post-stratification
                            landarea = "FOREST",                # est - forest land filter
                            sumunits = TRUE,                    # est - sum estimation units to population
                            estvar = "TPA_UNADJ",               # est - number of trees per acre, numerator
                            estvar.filter = "STATUSCD == 1",    # est - live trees only, numerator
                            rowvar = "FORTYPCD",                # est - row domain
                            table_opts = table_options(
                              row.FIAname = TRUE,               # est - row domain names
                              allin1 = FALSE                    # out - return output with est(pse)
                            ))

    ## Number of live trees by species, Wyoming, 2011-2013, no post-stratification
    tree.nostrat <- modGBtree(GBpopdat = GBpopdat.nostrat,      # pop - population calculations for WY, no post-stratification
                              landarea = "FOREST",              # est - forest land filter
                              sumunits = TRUE,                  # est - sum estimation units to population
                              estvar = "TPA_UNADJ",             # est - number of trees per acre, numerator
                              estvar.filter = "STATUSCD == 1",  # est - live trees only, numerator
                              rowvar = "FORTYPCD",              # est - row domain
                              table_opts = table_options(
                                row.FIAname = TRUE,             # est - row domain names
                                allin1 = FALSE                  # out - return output with est(pse)
                              ))

    # Compare estimates and percent standard errors with and without post-stratification
    head(tree.strat$est)

output

    ##                 Forest type    Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper  94791336.1                  24.57
    ## 2          Juniper woodland    54049570                  28.36
    ## 3 Pinyon / juniper woodland   1072754.3                    100
    ## 4               Douglas-fir 240480740.8                  21.25
    ## 5            Ponderosa pine 260800542.8                  17.27
    ## 6          Engelmann spruce   201830027                  25.03

    head(tree.nostrat$est)

output

    ##                 Forest type    Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper  95715598.7                  24.12
    ## 2          Juniper woodland  54055007.6                  28.29
    ## 3 Pinyon / juniper woodland   1072754.3                    100
    ## 4               Douglas-fir 238146030.4                  21.41
    ## 5            Ponderosa pine 271234746.9                  17.55
    ## 6          Engelmann spruce 203591491.9                  25.02

    ## Number of live trees per acre by species, Wyoming, 2011-2013, with post-stratification
    ratio.strat <- modGBratio(GBpopdat = GBpopdat.strat,           # pop - population calculations for WY, post-stratification
                              landarea = "FOREST",                 # est - forest land filter
                              sumunits = TRUE,                     # est - sum estimation units to population
                              estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
                              estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
                              rowvar = "FORTYPCD",                 # est - row domain
                              table_opts = table_options(
                                row.FIAname = TRUE,                # est - row domain names
                                allin1 = FALSE                     # out - return output with est(pse)
                              ))

    ## Number of live trees per acre by species, Wyoming, 2011-2013, no post-stratification
    ratio.nostrat <- modGBratio(GBpopdat = GBpopdat.nostrat,       # pop - population calculations for WY, no post-stratification
                                landarea = "FOREST",               # est - forest land filter
                                sumunits = TRUE,                   # est - sum estimation units to population
                                estvarn = "TPA_UNADJ",             # est - number of trees per acre, numerator
                                estvarn.filter = "STATUSCD == 1",  # est - live trees only, numerator
                                rowvar = "FORTYPCD",               # est - row domain
                                table_opts = table_options(
                                  row.FIAname = TRUE,              # est - row domain names
                                  allin1 = FALSE                   # out - return output with est(pse)
                                ))

    # Compare estimates and percent standard errors with and without post-stratification
    head(ratio.strat$est)

output

    ##                 Forest type Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper    149.9                  17.49
    ## 2          Juniper woodland    159.1                   14.1
    ## 3 Pinyon / juniper woodland     72.2                      0
    ## 4               Douglas-fir    272.9                  15.15
    ## 5            Ponderosa pine    293.2                  11.01
    ## 6          Engelmann spruce      432                  15.51

    head(ratio.nostrat$est)

output

    ##                 Forest type Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper    150.1                   17.1
    ## 2          Juniper woodland    158.9                  14.06
    ## 3 Pinyon / juniper woodland     72.2                      0
    ## 4               Douglas-fir    275.7                  15.09
    ## 5            Ponderosa pine    287.2                  10.79
    ## 6          Engelmann spruce    430.2                  15.34

### By estimation unit (`sumunits = FALSE`)

If you just wanted estimates by estimation unit and did not want to sum them, set `sumunits = FALSE`. If `sumunits = FALSE`, the estimates and percent standard errors returned are by estimation unit, with an attribute, named ‘unit’ appended to data frame, with the unit value. The raw data returned will look the same as if `sumunits = TRUE`.

View Code

    area.unit <- modGBarea(GBpopdat = GBpopdat,         # pop - population calculations for WY, post-stratification
                           landarea = "FOREST",         # est - forest land filter
                           sumunits = FALSE,            # est - sum estimation units to population
                           rowvar = "FORTYPCD",         # est - row domain
                           colvar = "STDSZCD",          # est - column domain
                           returntitle = TRUE,          # out - return title information
                           table_opts = table_options(
                             allin1 = TRUE              # out - return output with est(pse)
                           ))

    # Estimate and percent sampling error of estimate (first 6 rows)
    head(area.unit$est)

output

    ##   unit Forest type                 1           2                 3           5
    ## 1    1         182 19,698.3 (102.67) -- (    --) 19,698.3 (102.67) -- (    --)
    ## 2    1         184       -- (    --) -- (    --)       -- (    --) -- (    --)
    ## 3    1         185       -- (    --) -- (    --)       -- (    --) -- (    --)
    ## 4    1         201 28,215.6 ( 87.92) -- (    --)       -- (    --) -- (    --)
    ## 5    1         221 89,329.6 ( 45.13) -- (    --)       -- (    --) -- (    --)
    ## 6    1         265       -- (    --) -- (    --)       -- (    --) -- (    --)
    ##            NA              Total
    ## 1 -- (    --)  39,396.6 ( 72.29)
    ## 2 -- (    --)  28,215.6 ( 87.92)
    ## 3 -- (    --)  89,329.6 ( 45.13)
    ## 4 -- (    --) 112,862.2 ( 39.62)
    ## 5 -- (    --)  28,215.6 ( 87.92)
    ## 6 -- (    --) 106,303.3 ( 39.92)

    # Unique estimation units
    unique(area.unit$est$unit)

output

    ##  [1] "1"  "11" "13" "15" "17" "19" "21" "23" "25" "27" "29" "3"  "31" "33" "35"
    ## [16] "37" "39" "41" "43" "45" "5"  "7"  "9"

## References

Bechtold, William A.; Patterson, Paul L., Editors. 2005. The enhanced Forest Inventory and Analysis program national sampling design and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 85 p.

Patterson, Paul L. 2012. Photo-based estimators for the Nevada photo-based inventory. Res. Pap. RMRS-RP-92. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 14 p.

Westfall, James A.; Patterson, Paul L.; Coulston, John W. 2011. Post-stratified estimation: with-in strata and total sample size recommendations. Canadian Journal of Forest Research. 41: 1130-1139.