# Model-Assisted Estimators

## Model-Assisted (MA) module overview

`FIESTA`’s Model-Assisted (MA) module calculates population estimates and their sampling errors by taking advantage of available model-assisted survey estimators from the `mase` R package (McConville, et al. 2018). These estimators can use a variety of auxiliary data to build models and predict over a response variable of interest, while using a bias-correction term so that the bias of the model does not depend on model misspecification.

Functions in `FIESTA` used for fitting model-assisted estimators include the `modMAarea` function for area estimates and `modMAtree` for tree estimates. The `modMApop` function is used to get population data needed for model-assisted estimation. Below is a description and table of contents for the sections related to these functions:

FUNCTION | DESCRIPTION
--- | ---
modMApop | Creates population data for model-assisted estimation.
modMAarea | Produces area level estimates through model-assisted estimation.
modMAtree | Produces tree level estimates through model-assisted estimation.

## Objective of tutorial

The main objective of this tutorial is to demonstrate how to use `FIESTA` for generating estimates using estimators from `mase`. The model-assisted 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 following examples are for generating estimates and estimated variances using standard FIA Evaluation data from FIA’s National database, with custom Estimation unit and Stratification information. The examples use data from 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`.

## Example data - Wyoming (WY), Inventory Years 2011-2012

View MA Example Data

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

External data | Description
--- | ---
WYbighorn_adminbnd.shp | Polygon shapefile of WY Bighorn National Forest Administrative boundary*
WYbighorn_districtbnd.shp | Polygon shapefile of WY Bighorn National Forest District boundaries**
WYbighorn_forest_nonforest_250m.tif | GeoTIFF raster of predicted forest/nonforest (1/0) for stratification***
WYbighorn_dem_250m.img | Erdas Imagine raster of elevation change, in meters****

*USDA 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.

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

***Based on MODIS-based classified map resampled from 250m to 500m resolution and reclassified from 3 to 2 classes: 1:forest; 2:nonforest. Projected in Albers Conical Equal Area, Datum NAD27 (Ruefenacht et al. 2008). Clipped to extent of WYbighorn_adminbnd.shp.

****USGS National Elevation Dataset (NED), resampled from 30m resolution to 250m. Projected in Albers Conical Equal Area, Datum NAD27 (U.S. Geological Survey 2017). Clipped to boundary of WYbighorn_adminbnd.shp.

### 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 data for examples

View Getting Data

Now that we’ve loaded `FIESTA` and setup our outfolder, we can retrieve the data needed to run the examples. First, we point to some external data and predictor layers stored in `FIESTA` and derive new predictor layers using the `terra` package.

    # File names for external spatial data
    WYbhfn <- system.file("extdata", "sp_data/WYbighorn_adminbnd.shp",
                          package = "FIESTA")

    WYbhdistfn <- system.file("extdata", "sp_data/WYbighorn_districtbnd.shp",
                              package = "FIESTA")

    ## predictor variables
    fornffn <- system.file("extdata", "sp_data/WYbighorn_forest_nonforest_250m.tif",
                           package = "FIESTA")

    demfn <- system.file("extdata", "sp_data/WYbighorn_dem_250m.img",
                         package = "FIESTA")

    # Derive new predictor layers from dem
    library(terra)

    dem <- rast(demfn)
    slpfn <- paste0(outfolder, "/WYbh_slp.img")
    slp <- terra::terrain(dem,
                          v = "slope",
                          unit = "degrees",
                          filename = slpfn,
                          overwrite = TRUE,
                          NAflag = -99999.0)

    aspfn <- paste0(outfolder, "/WYbh_asp.img")
    asp <- terra::terrain(dem,
                          v = "aspect",
                          unit = "degrees",
                          filename = aspfn,
                          overwrite = TRUE,
                          NAflag = -99999.0)

Next, we can get our FIA plot data and set up our auxiliary data. We can get our FIA plot data with the `spMakeSpatialPoints` function from `FIESTA`.

    WYspplt <- spMakeSpatialPoints(xyplt = WYplt,
                                   xy.uniqueid = "CN",
                                   xvar = "LON_PUBLIC",
                                   yvar = "LAT_PUBLIC",
                                   xy.crs = 4269)

    rastlst.cont <- c(demfn, slpfn, aspfn)
    rastlst.cont.name <- c("dem", "slp", "asp")
    rastlst.cat <- fornffn
    rastlst.cat.name <- "fornf"

Next, we must prepare auxiliary data for model-assisted estimation. We can do this with the `spGetAuxiliary` function from `FIESTA`. See the `sp` vignette for further information on this function.

    modeldat <- spGetAuxiliary(xyplt = WYspplt,
                               uniqueid = "CN",
                               unit_layer = WYbhfn,
                               unitvar = NULL,
                               rastlst.cont = rastlst.cont,
                               rastlst.cont.name = rastlst.cont.name,
                               rastlst.cat = rastlst.cat,
                               rastlst.cat.name = rastlst.cat.name,
                               rastlst.cont.stat = "mean",
                               asptransform = TRUE,
                               rast.asp = aspfn,
                               keepNA = FALSE,
                               showext = FALSE,
                               savedata = FALSE)

    str(modeldat, max.level = 1)

output

    ## List of 12
    ##  $ unitvar       : chr "ONEUNIT"
    ##  $ pltassgn      :'data.frame':  56 obs. of  25 variables:
    ##  $ pltassgnid    : chr "CN"
    ##  $ unitarea      :'data.frame':  1 obs. of  2 variables:
    ##  $ areavar       : chr "ACRES_GIS"
    ##  $ unitzonal     :'data.frame':  1 obs. of  10 variables:
    ##  $ inputdf       :Classes 'data.table' and 'data.frame': 4 obs. of  7 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##  $ prednames     : chr [1:5] "dem" "slp" "asp_cos" "asp_sin" ...
    ##  $ zonalnames    : chr [1:7] "dem" "slp" "asp_cos" "asp_sin" ...
    ##  $ predfac       : chr "fornf"
    ##  $ npixelvar     : chr "npixels"
    ##  $ predfac.levels:List of 1

## Examples

### `modMApop`

#### Example 1: Creating our population dataset with `modMApop`

View Example

We can create our population data for model-assisted estimation. To do so, we use the `modMApop` function in `FIESTA`. We must assign our population tables with the `popTabs` argument (and unique identifiers for these tables with the `popTabIDs` argument if they are not the default). Because we used `spGetAuxiliary` to create our model data we can simply pass the object returned by that function into the `auxdat` argument in `modMApop`. This is a shortcut that allows you to avoid manually specifying all of the necessary tables as function arguments in `modMApop`.

    MApopdat <- modMApop(popTabs = popTables(tree = WYtree,
                                             cond = WYcond,
                                             plt = WYplt),
                         auxdat = modeldat)

Note that the `modMApop` function returns a list with lots of information and data for us to use. For a quick look at what this list includes we can use the `str` function:

    str(MApopdat, max.level = 1)

output

    ## List of 40
    ##  $ module       : chr "MA"
    ##  $ popType      : chr "VOL"
    ##  $ pltidsadj    :Classes 'data.table' and 'data.frame':  56 obs. of  5 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= Named chr "CN"
    ##   .. ..- attr(*, "names")= chr "CN"
    ##  $ pltcondx     :Classes 'data.table' and 'data.frame':  67 obs. of  37 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr [1:2] "PLT_CN" "CONDID"
    ##  $ pltcondflds  : chr [1:36] "PLT_CN" "CONDID" "COND_NONSAMPLE_REASN_CD" "CONDPROP_UNADJ" ...
    ##  $ pjoinid      : Named chr "CN"
    ##   ..- attr(*, "names")= chr "CN"
    ##  $ cuniqueid    : chr "PLT_CN"
    ##  $ pltassgnid   : Named chr "CN"
    ##   ..- attr(*, "names")= chr "CN"
    ##  $ condid       : chr "CONDID"
    ##  $ ACI          : logi FALSE
    ##  $ areawt       : chr "CONDPROP_UNADJ"
    ##  $ areawt2      : NULL
    ##  $ adjcase      : Named chr "ADJ_FACTOR_COND"
    ##   ..- attr(*, "names")= chr "COND"
    ##  $ dbqueries    :List of 4
    ##  $ dbqueriesWITH:List of 4
    ##  $ pltassgnx    :Classes 'data.table' and 'data.frame':  56 obs. of  8 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= Named chr "CN"
    ##   .. ..- attr(*, "names")= chr "CN"
    ##  $ unitlut      :Classes 'data.table' and 'data.frame':  1 obs. of  9 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##  $ unitarea     :Classes 'data.table' and 'data.frame':  1 obs. of  2 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr "ONEUNIT"
    ##  $ npixels      :Classes 'data.table' and 'data.frame':  1 obs. of  2 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##  $ npixelvar    : chr "npixels"
    ##  $ estvar.area  : chr "CONDPROP_ADJ"
    ##  $ areavar      : chr "ACRES_GIS"
    ##  $ areaunits    : chr "acres"
    ##  $ unitvar      : chr "ONEUNIT"
    ##  $ unitvars     : chr "ONEUNIT"
    ##  $ plotsampcnt  :'data.frame':   2 obs. of  3 variables:
    ##  $ condsampcnt  :'data.frame':   4 obs. of  3 variables:
    ##  $ states       : chr "Wyoming"
    ##  $ invyrs       : int [1:3] 2011 2012 2013
    ##  $ adj          : chr "plot"
    ##  $ P2POINTCNT   :'data.frame':   0 obs. of  0 variables
    ##  $ plotunitcnt  :'data.frame':   1 obs. of  4 variables:
    ##  $ treex        :Classes 'data.table' and 'data.frame':  1706 obs. of  19 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= chr [1:4] "PLT_CN" "CONDID" "SUBP" "TREE"
    ##  $ tuniqueid    : chr "PLT_CN"
    ##  $ adjtree      : logi FALSE
    ##  $ adjfactors   :Classes 'data.table' and 'data.frame':  56 obs. of  5 variables:
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..- attr(*, "sorted")= Named chr "CN"
    ##   .. ..- attr(*, "names")= chr "CN"
    ##  $ adjvarlst    : Named chr [1:4] "ADJ_FACTOR_COND" "ADJ_FACTOR_SUBP" "ADJ_FACTOR_MACR" "ADJ_FACTOR_MICR"
    ##   ..- attr(*, "names")= chr [1:4] "COND" "SUBP" "MACR" "MICR"
    ##  $ prednames    : chr [1:5] "dem" "slp" "asp_cos" "asp_sin" ...
    ##  $ predfac      : chr "fornf"
    ##  $ popdatindb   : logi FALSE

Now that we’ve created our population data set, we can move on to estimation.

### `modMAarea`

#### Example 2: Area of forest land, Wyoming, 2011-2013

View Example

In this example, we look at estimating the area of forest land in Wyoming from 2011 to 2013 with the generalized regression estimator (`MAmethod = "greg"`). We can specify a set of auxiliary variables that we want to use in the model using the `prednames` argument.

    area1 <- modMAarea(MApopdat = MApopdat,             # pop - population calculations for WY
                       MAmethod = "greg",               # est - model-assisted method
                       prednames = c("dem", "fornf"),   # est - predictors to use in model
                       landarea = "FOREST")             # est - forest land filter

We can look at the structure of this output with `str` and the estimates below. Note that again `FIESTA` outputs a list.

    str(area1, max.level = 2)

output

    ## List of 5
    ##  $ est    :'data.frame': 1 obs. of  3 variables:
    ##   ..$ ONEUNIT               : chr "1"
    ##   ..$ Estimate              : num 1029015
    ##   ..$ Percent Sampling Error: num 3.93
    ##  $ raw    :List of 10
    ##   ..$ unit_totest  :'data.frame':    1 obs. of  17 variables:
    ##   ..$ domdat       :'data.frame':    40 obs. of  4 variables:
    ##   ..$ plotweights  :Classes 'data.table' and 'data.frame':   37 obs. of  5 variables:
    ##   .. ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..$ module       : chr "MA"
    ##   ..$ esttype      : chr "AREA"
    ##   ..$ MAmethod     : chr "greg"
    ##   ..$ predselectlst:List of 1
    ##   ..$ rowvar       : chr "TOTAL"
    ##   ..$ colvar       : chr "NONE"
    ##   ..$ areaunits    : chr "acres"
    ##  $ statecd: int 56
    ##  $ states : chr "Wyoming"
    ##  $ invyr  : int [1:3] 2011 2012 2013

    area1$est

output

    ##   ONEUNIT Estimate Percent Sampling Error
    ## 1       1  1029015                   3.93

#### Example 3: Area of forest land, Wyoming, 2011-2013, using the Elastic Net for variable selection

View Example

Here, we fit the same model as the above example, but we don’t specify predictors and instead include `modelselect = TRUE` which internally uses an elastic net model for variable selection.

    area2 <- modMAarea(MApopdat = MApopdat,          # pop - population calculations for WY
                       MAmethod = "greg",
                       modelselect = TRUE,           # est - model-assisted method
                       landarea = "FOREST")          # est - forest land filter

We can again see that the structure of the list is very similar to that in the above example:

    str(area2, max.level = 2)

output

    ## List of 5
    ##  $ est    :'data.frame': 1 obs. of  3 variables:
    ##   ..$ ONEUNIT               : chr "1"
    ##   ..$ Estimate              : num 1036942
    ##   ..$ Percent Sampling Error: num 3.76
    ##  $ raw    :List of 11
    ##   ..$ unit_totest       :'data.frame':   1 obs. of  17 variables:
    ##   ..$ domdat            :'data.frame':   40 obs. of  4 variables:
    ##   ..$ plotweights       :Classes 'data.table' and 'data.frame':  37 obs. of  5 variables:
    ##   .. ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..$ module            : chr "MA"
    ##   ..$ esttype           : chr "AREA"
    ##   ..$ MAmethod          : chr "greg"
    ##   ..$ predselectlst     :List of 1
    ##   ..$ predselect.overall:Classes 'data.table' and 'data.frame':  1 obs. of  5 variables:
    ##   .. ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..$ rowvar            : chr "TOTAL"
    ##   ..$ colvar            : chr "NONE"
    ##   ..$ areaunits         : chr "acres"
    ##  $ statecd: int 56
    ##  $ states : chr "Wyoming"
    ##  $ invyr  : int [1:3] 2011 2012 2013

However now the `raw` list has an item called `predselectlst` which stores information on the predictors that were selected.

    area2$raw$predselectlst$totest

output

    ##    ONEUNIT TOTAL         dem       slp     asp_cos      asp_sin    fornf2
    ##     <fctr> <int>       <num>     <num>       <num>        <num>     <num>
    ## 1:       1     1 -0.04691924 0.0168218 -0.06238766 -0.008714709 0.0448006

And finally we can view the actual estimate:

    area2$est

output

    ##   ONEUNIT Estimate Percent Sampling Error
    ## 1       1  1036942                   3.76

#### Example 4: 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. Note that when we do not explicitly supply `prednames` and do not set `modelselect` to TRUE, FIESTA defaults to using all of the available predictors.

    area3 <- modMAarea(MApopdat = MApopdat,          # pop - population calculations for WY, post-stratification
                       MAmethod = "greg",            # est - model-assisted method
                       landarea = "FOREST",          # est - forest land filter
                       rowvar = "FORTYPCD",          # est - row domain
                       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.

    str(area3, max.level = 1)

output

    ## List of 6
    ##  $ est     :'data.frame':    9 obs. of  3 variables:
    ##  $ titlelst:List of 10
    ##  $ raw     :List of 11
    ##  $ statecd : int 56
    ##  $ states  : chr "Wyoming"
    ##  $ invyr   : int [1:3] 2011 2012 2013

And the estimates:

    area3$est

output

    ##   Forest type Estimate Percent Sampling Error
    ## 1         201  44286.4                   85.2
    ## 2         265  89101.3                  52.03
    ## 3         266  97017.3                   42.4
    ## 4         268 112697.6                  34.19
    ## 5         281 618425.6                  13.59
    ## 6         901  38648.7                  75.39
    ## 7         999    36765                  64.92
    ## 8        <NA>       --                     --
    ## 9       Total  1036942                   3.76

Along with raw data and titles:

    raw3 <- area3$raw      # extract raw data list object from output
    names(raw3)

output

    ##  [1] "unit_totest"   "unit_rowest"   "domdat"        "plotweights"
    ##  [5] "module"        "esttype"       "MAmethod"      "predselectlst"
    ##  [9] "rowvar"        "colvar"        "areaunits"

    head(raw3$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT      nhat    nhat.var NBRPLT NBRPLT.gt0 AREAUSED     est    est.var
    ## 1       1 0.9321562 0.001228216     37         37  1112412 1036942 1519869489
    ##    est.se     est.cv      pse CI99left CI99right CI95left CI95right CI68left
    ## 1 38985.5 0.03759661 3.759661   936522   1137362 960531.9   1113352 998172.6
    ##   CI68right
    ## 1   1075711

    head(raw3$unit_rowest) # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT Forest type       nhat     nhat.var NBRPLT NBRPLT.gt0 AREAUSED
    ## 1       1         201 0.03981113 0.0011505987     37          2  1112412
    ## 2       1         265 0.08009738 0.0017367503     37          4  1112412
    ## 3       1         266 0.08721348 0.0013673266     37          5  1112412
    ## 4       1         268 0.10130923 0.0012000844     37          3  1112412
    ## 5       1         281 0.55593198 0.0057107487     37         23  1112412
    ## 6       1         901 0.03474316 0.0006861348     37          1  1112412
    ##         est    est.var   est.se    est.cv      pse  CI99left CI99right
    ## 1  44286.39 1423821285 37733.56 0.8520351 85.20351      0.00  141481.6
    ## 2  89101.31 2149161132 46359.05 0.5202959 52.02959      0.00  208514.3
    ## 3  97017.35 1692013618 41134.09 0.4239870 42.39870      0.00  202971.7
    ## 4 112697.64 1485057928 38536.45 0.3419455 34.19455  13434.33  211961.0
    ## 5 618425.60 7066830141 84064.44 0.1359330 13.59330 401889.95  834961.3
    ## 6  38648.72  849065242 29138.72 0.7539376 75.39376      0.00  113705.1
    ##    CI95left CI95right   CI68left CI68right
    ## 1      0.00 118242.80   6761.956  81810.82
    ## 2      0.00 179963.37  42999.193 135203.43
    ## 3  16396.01 177638.69  56111.224 137923.47
    ## 4  37167.59 188227.70  74374.767 151020.52
    ## 5 453662.33 783188.88 534827.056 702024.15
    ## 6      0.00  95759.57   9671.489  67625.96

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

output

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

    titlelst3

output

    ## $title.est
    ## [1] "Area, in acres, on forest land by forest type"
    ##
    ## $title.pse
    ## [1] "Percent sampling error of area, in acres, on forest land by forest type"
    ##
    ## $title.unitvar
    ## [1] "ONEUNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $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; Wyoming, 2011-2013"
    ##
    ## $title.unit
    ## [1] "acres"

#### Example 5: Area by forest type and stand-size class on forest land, Wyoming, 2011-2013

View Example

In this example, we look at adding rows and columns to output, including FIA names. 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`.

    area4 <- modMAarea(MApopdat = MApopdat,                 # pop - population calculations for WY, post-stratification
                       MAmethod = "greg",                   # est - model-assisted method
                       landarea = "FOREST",                 # est - forest land filter
                       rowvar = "FORTYPCD",                 # est - row domain
                       colvar = "STDSZCD",                  # est - column domain
                       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(area4)

output

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

    # Estimate and percent sampling error of estimate
    head(area4$est)

output

    ##                        Forest type      Large diameter     Medium diameter
    ## 1                      Douglas-fir  28,381.5 (  95.01)        -- (     --)
    ## 2                 Engelmann spruce  97,387.6 (  40.28)  -8,286.3 (-339.39)
    ## 3 Engelmann spruce / subalpine fir  42,290.6 (  91.23)  28,627.2 (  48.64)
    ## 4                    Subalpine fir  36,053.1 (  81.54)        -- (     --)
    ## 5                   Lodgepole pine 221,069.2 (  28.62) 289,086.8 (  20.06)
    ## 6                            Aspen        -- (     --)        -- (     --)
    ##        Small diameter   Nonstocked               Total
    ## 1  15,904.9 ( 179.36) -- (     --)  38,648.7 (  75.39)
    ## 2        -- (     --) -- (     --)  44,286.4 (  85.20)
    ## 3  26,099.5 (  65.28) -- (     --)  89,101.3 (  52.03)
    ## 4  76,644.6 (  33.27) -- (     --)  97,017.3 (  42.40)
    ## 5 108,269.6 (  35.83) -- (     --) 618,425.6 (  13.59)
    ## 6  38,648.7 (  75.39) -- (     --)  36,765.0 (  64.92)

    # Raw data (list object) for estimate
    raw4 <- area4$raw      # extract raw data list object from output
    names(raw4)

output

    ##  [1] "unit_totest"   "unit_rowest"   "unit_colest"   "unit_grpest"
    ##  [5] "domdat"        "plotweights"   "module"        "esttype"
    ##  [9] "MAmethod"      "predselectlst" "rowvar"        "colvar"
    ## [13] "areaunits"

    head(raw4$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT      nhat    nhat.var NBRPLT NBRPLT.gt0 AREAUSED     est    est.var
    ## 1       1 0.9321562 0.001228216     37         37  1112412 1036942 1519869489
    ##    est.se     est.cv      pse CI99left CI99right CI95left CI95right CI68left
    ## 1 38985.5 0.03759661 3.759661   936522   1137362 960531.9   1113352 998172.6
    ##   CI68right
    ## 1   1075711

    head(raw4$unit_rowest) # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT FORTYPCD       nhat     nhat.var NBRPLT NBRPLT.gt0
    ## 1       1      201 0.03981113 0.0011505987     37          2
    ## 2       1      265 0.08009738 0.0017367503     37          4
    ## 3       1      266 0.08721348 0.0013673266     37          5
    ## 4       1      268 0.10130923 0.0012000844     37          3
    ## 5       1      281 0.55593198 0.0057107487     37         23
    ## 6       1      901 0.03474316 0.0006861348     37          1
    ##                        Forest type AREAUSED       est    est.var   est.se
    ## 1                      Douglas-fir  1112412  44286.39 1423821285 37733.56
    ## 2                 Engelmann spruce  1112412  89101.31 2149161132 46359.05
    ## 3 Engelmann spruce / subalpine fir  1112412  97017.35 1692013618 41134.09
    ## 4                    Subalpine fir  1112412 112697.64 1485057928 38536.45
    ## 5                   Lodgepole pine  1112412 618425.60 7066830141 84064.44
    ## 6                            Aspen  1112412  38648.72  849065242 29138.72
    ##      est.cv      pse  CI99left CI99right  CI95left CI95right   CI68left
    ## 1 0.8520351 85.20351      0.00  141481.6      0.00 118242.80   6761.956
    ## 2 0.5202959 52.02959      0.00  208514.3      0.00 179963.37  42999.193
    ## 3 0.4239870 42.39870      0.00  202971.7  16396.01 177638.69  56111.224
    ## 4 0.3419455 34.19455  13434.33  211961.0  37167.59 188227.70  74374.767
    ## 5 0.1359330 13.59330 401889.95  834961.3 453662.33 783188.88 534827.056
    ## 6 0.7539376 75.39376      0.00  113705.1      0.00  95759.57   9671.489
    ##   CI68right
    ## 1  81810.82
    ## 2 135203.43
    ## 3 137923.47
    ## 4 151020.52
    ## 5 702024.15
    ## 6  67625.96

    head(raw4$unit_colest) # estimates by column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT STDSZCD       nhat     nhat.var NBRPLT NBRPLT.gt0 Stand-size class
    ## 1       1       1 0.38221627 0.0033901272     37         18   Large diameter
    ## 2       1       2 0.27815912 0.0030155927     37         14  Medium diameter
    ## 3       1       3 0.23873097 0.0024143561     37          6   Small diameter
    ## 4       1       5 0.03304982 0.0004603932     37          1       Nonstocked
    ##   AREAUSED       est    est.var   est.se    est.cv      pse CI99left CI99right
    ## 1  1112412 425182.10 4195150958 64769.99 0.1523347 15.23347 258345.7 592018.52
    ## 2  1112412 309427.64 3731679124 61087.47 0.1974209 19.74209 152076.7 466778.53
    ## 3  1112412 265567.28 2987672110 54659.60 0.2058220 20.58220 124773.5 406361.09
    ## 4  1112412  36765.03  569718693 23868.78 0.6492251 64.92251      0.0  98246.94
    ##   CI95left CI95right  CI68left CI68right
    ## 1 298235.3 552128.93 360771.07 489593.12
    ## 2 189698.4 429156.88 248678.72 370176.55
    ## 3 158436.4 372698.13 211210.61 319923.96
    ## 4      0.0  83546.98  13028.53  60501.53

    head(raw4$unit_grpest) # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT grpvar         nhat     nhat.var NBRPLT NBRPLT.gt0 FORTYPCD STDSZCD
    ## 1       1  201#1  0.025513469 0.0005875578     37          1      201       1
    ## 2       1  201#3  0.014297657 0.0006576539     37          1      201       3
    ## 3       1  265#1  0.087546327 0.0012436962     37          3      265       1
    ## 4       1  265#2 -0.007448949 0.0006391340     37          1      265       2
    ## 5       1  266#1  0.038017056 0.0012029502     37          3      266       1
    ## 6       1  266#2  0.025734305 0.0001566975     37          1      266       2
    ##   Stand-size class                      Forest type AREAUSED       est
    ## 1   Large diameter                      Douglas-fir  1112412 28381.499
    ## 2   Small diameter                      Douglas-fir  1112412 15904.890
    ## 3   Large diameter                 Engelmann spruce  1112412 97387.615
    ## 4  Medium diameter                 Engelmann spruce  1112412 -8286.303
    ## 5   Large diameter Engelmann spruce / subalpine fir  1112412 42290.643
    ## 6  Medium diameter Engelmann spruce / subalpine fir  1112412 28627.159
    ##      est.var   est.se     est.cv        pse CI99left CI99right  CI95left
    ## 1  727079950 26964.42  0.9500703   95.00703        0  97837.24     0.000
    ## 2  813821229 28527.55  1.7936340  179.36340        0  89386.99     0.000
    ## 3 1539025822 39230.42  0.4028276   40.28276        0 198438.48 20497.406
    ## 4  790903534 28123.01 -3.3939149 -339.39149        0  64153.76     0.000
    ## 5 1488604221 38582.43  0.9123161   91.23161        0 141672.41     0.000
    ## 6  193907140 13925.05  0.4864281   48.64281        0  64495.72  1334.554
    ##   CI95right  CI68left CI68right
    ## 1  81230.79  1566.518  55196.48
    ## 2  71817.86     0.000  44274.34
    ## 3 174277.82 58374.615 136400.62
    ## 4  46833.78     0.000  19680.84
    ## 5 117910.82  3922.037  80659.25
    ## 6  55919.76 14779.279  42475.04

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

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"

    titlelst4

output

    ## $title.estpse
    ## [1] "Area, in acres (percent sampling error), by forest type and stand-size class on forest land"
    ##
    ## $title.unitvar
    ## [1] "ONEUNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $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; Wyoming, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Area, in acres (percent sampling error), by stand-size class on forest land; Wyoming, 2011-2013"
    ##
    ## $title.unit
    ## [1] "acres"

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

output

    ## [1] "WY_area_FORTYPCD_STDSZCD_forestland.csv"
    ## [2] "WY_area_FORTYPCD_STDSZCD_forestland_modMA_mase_greg.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_modMA_mase_greg_domdat.csv"
    ##  [5] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_plotweights.csv"
    ##  [6] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_colest.csv"
    ##  [7] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_grpest.csv"
    ##  [8] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_rowest.csv"
    ##  [9] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_totest.csv"
    ## [10] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_rowest.csv"
    ## [11] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_totest.csv"
    ## [12] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_colest.csv"
    ## [13] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_grpest.csv"
    ## [14] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_rowest.csv"
    ## [15] "WY_area_FORTYPCD_STDSZCD_forestland_rawdata_unit_totest.csv"

### `modMAtree`

We now transition to from generating estimates of area to estimates of tree attributes using the `modMAtree` function. This requires that we set our estimate variable and filter. We set `estvar` to `"VOLCFNET"` for net cubic foot volume, and filter with `estvar.filter` set to `"STATUSCD == 1"` so we only consider live trees in our estimation.

    estvar <- "VOLCFNET"
    live_trees <- "STATUSCD == 1"

#### Example 6: Net cubic-foot volume of live trees, Wyoming, 2011-2013

View Example

    tree1 <- modMAtree(MApopdat = MApopdat,            # pop - population calculations
                       MAmethod = "greg",              # est - model-assisted method
                       landarea = "FOREST",            # est - forest land filter
                       prednames = c("dem", "fornf"),  # est - predictors to use in model
                       estvar = estvar,                # est - net cubic-foot volume
                       estvar.filter = live_trees,     # est - live trees only
                       returntitle = TRUE)             # out - return title information

    tree1$est

output

    ##   ONEUNIT   Estimate Percent Sampling Error
    ## 1       1 1931700102                   11.2

#### Example 7: Net cubic-foot volume of live trees, Wyoming, 2011-2013, using the Elastic Net for variable selection

View Example

Here, we fit the same model as the above example, but rather than using `"greg"` are our model-assisted method, we can use `"gregEN"` where the EN stands for “elastic net”. The elastic net performs variable selection for us, grabbing predictors it finds to be most useful in the model.

    tree2 <- modMAtree(MApopdat = MApopdat,            # pop - population calculations
                       MAmethod = "greg",              # est - model-assisted method
                       modelselect = TRUE,             # est - perform variable selection internally
                       landarea = "FOREST",            # est - forest land filter
                       estvar = estvar,                # est - net cubic-foot volume
                       estvar.filter = live_trees,     # est - live trees only
                       returntitle = TRUE)             # out - return title information

We can again see that the structure of the list is very similar to that in the above example:

    str(tree2, max.level = 2)

output

    ## List of 6
    ##  $ est     :'data.frame':    1 obs. of  3 variables:
    ##   ..$ ONEUNIT               : chr "1"
    ##   ..$ Estimate              : num 1602795606
    ##   ..$ Percent Sampling Error: num 11.6
    ##  $ titlelst:List of 10
    ##   ..$ title.estpse : chr "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet, and perce"| __truncated__
    ##   ..$ title.yvar   : chr "Net volume, in cubic feet"
    ##   ..$ title.estvar : chr "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia)"
    ##   ..$ title.unitvar: chr "ONEUNIT"
    ##   ..$ title.ref    : chr "Wyoming, 2011-2013"
    ##   ..$ outfn.estpse : chr "tree_VOLCFNET_forestland"
    ##   ..$ outfn.rawdat : chr "tree_VOLCFNET_forestland_rawdata"
    ##   ..$ outfn.param  : chr "tree_VOLCFNET_forestland_parameters"
    ##   ..$ title.tot    : chr "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet, on forest"| __truncated__
    ##   ..$ title.unit   : chr "cubic feet"
    ##  $ raw     :List of 14
    ##   ..$ unit_totest       :'data.frame':   1 obs. of  17 variables:
    ##   ..$ domdat            :'data.frame':   40 obs. of  5 variables:
    ##   ..$ plotweights       :Classes 'data.table' and 'data.frame':  37 obs. of  5 variables:
    ##   .. ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..$ estvar            : chr "VOLCFNET"
    ##   ..$ estvar.filter     : chr "STATUSCD == 1"
    ##   ..$ module            : chr "MA"
    ##   ..$ esttype           : chr "TREE"
    ##   ..$ MAmethod          : chr "greg"
    ##   ..$ predselect.overall:Classes 'data.table' and 'data.frame':  1 obs. of  5 variables:
    ##   .. ..- attr(*, ".internal.selfref")=<externalptr>
    ##   ..$ predselectlst     :List of 1
    ##   ..$ rowvar            : chr "TOTAL"
    ##   ..$ colvar            : chr "NONE"
    ##   ..$ areaunits         : chr "acres"
    ##   ..$ estunits          : chr "cubic feet"
    ##  $ statecd : int 56
    ##  $ states  : chr "Wyoming"
    ##  $ invyr   : int [1:3] 2011 2012 2013

However now the `raw` list has an item call `predselectlst`. We can look at this item now:

    tree2$raw$predselectlst

output

    ## $totest
    ##    ONEUNIT TOTAL      dem       slp   asp_cos   asp_sin   fornf2
    ##     <fctr> <int>    <num>     <num>     <num>     <num>    <num>
    ## 1:       1     1 415.5025 -481.4989 -144.2504 -620.3723 -763.951

And finally, we can look at the estimate

    tree2$est

output

    ##   ONEUNIT   Estimate Percent Sampling Error
    ## 1       1 1602795606                  11.59

#### Example 8: 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`.

    tree3 <- modMAtree(MApopdat = MApopdat,               # pop - population calculations
                       MAmethod = "greg",                 # est - model-assisted method
                       prednames = c("dem", "fornf"),     # est - predictors to use in model
                       landarea = "FOREST",               # est - forest land filter
                       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:

    # Look at output list
    names(tree3)

output

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

    # Estimate and percent sampling error of estimate
    tree3$est

output

    ##   Forest type     Estimate Percent Sampling Error
    ## 1         201   14937765.2                  104.5
    ## 2         265  477236769.3                  38.99
    ## 3         266  202892988.5                  57.86
    ## 4         268   53707726.6                  90.31
    ## 5         281 1182924852.4                  18.33
    ## 6         901           --                     --
    ## 7         999           --                     --
    ## 8        <NA>           --                     --
    ## 9       Total   1931700102                   11.2

We can also create a simple barplot from the output:

    datBarplot(raw3$unit_rowest,
               xvar = titlelst3$title.rowvar,
               yvar = "est")

plot

And a fancier barplot:

    datBarplot(raw3$unit_rowest,
               xvar = titlelst3$title.rowvar,
               yvar = "est",
               errbars = TRUE,
               sevar = "est.se",
               main = FIESTAutils::wraptitle(titlelst3$title.row, 75),
               ylabel = titlelst3$title.yvar,
               divideby = "million")

plot

#### Example 9: 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`.

    tree4 <- modMAtree(MApopdat = MApopdat,                # pop - population calculations
                       MAmethod = "greg",                  # est - model-assisted method
                       landarea = "FOREST",                # est - forest land filter
                       prednames = c("dem", "slp"),        # est - predictors to use in model
                       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(tree4)

output

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

    # Estimate and percent sampling error of estimate
    tree4$est

output

    ##                        Forest type           Large diameter
    ## 1                      Douglas-fir    13,220,807.8 (110.84)
    ## 2                 Engelmann spruce   280,813,787.4 ( 56.81)
    ## 3 Engelmann spruce / subalpine fir   258,361,837.5 ( 46.33)
    ## 4                    Subalpine fir    64,151,931.3 ( 74.52)
    ## 5                   Lodgepole pine   843,091,898.5 ( 23.55)
    ## 6                            Aspen              -- (    --)
    ## 7                       Nonstocked              -- (    --)
    ## 8                            Other              -- (    --)
    ## 9                            Total 1,459,640,262.5 ( 14.80)
    ##          Medium diameter        Small diameter  Nonstocked
    ## 1            -- (    --)  3,232,360.3 (162.26) -- (    --)
    ## 2  91,643,840.9 (115.58)           -- (    --) -- (    --)
    ## 3  19,870,297.1 ( 73.46)  6,039,623.3 ( 88.17) -- (    --)
    ## 4            -- (    --)           -- (    --) -- (    --)
    ## 5 423,496,333.8 ( 28.44) 11,122,841.6 ( 61.94) -- (    --)
    ## 6            -- (    --)           -- (    --) -- (    --)
    ## 7            -- (    --)           -- (    --) -- (    --)
    ## 8            -- (    --)           -- (    --) -- (    --)
    ## 9 535,010,471.8 ( 26.77) 20,394,825.1 ( 45.99) -- (    --)
    ##                      Total
    ## 1              -- (    --)
    ## 2    16,453,168.1 ( 91.71)
    ## 3   372,457,628.3 ( 50.51)
    ## 4   284,271,757.8 ( 41.67)
    ## 5 1,277,711,073.9 ( 16.98)
    ## 6              -- (    --)
    ## 7              -- (    --)
    ## 8    64,151,931.3 ( 74.52)
    ## 9 2,015,045,559.4 ( 10.67)

    ## Raw data (list object) for estimate
    raw4 <- tree4$raw      # extract raw data list object from output
    names(raw4)

output

    ##  [1] "unit_totest"   "unit_rowest"   "unit_colest"   "unit_grpest"
    ##  [5] "domdat"        "plotweights"   "estvar"        "estvar.filter"
    ##  [9] "module"        "esttype"       "MAmethod"      "predselectlst"
    ## [13] "rowvar"        "colvar"        "areaunits"     "estunits"

    head(raw4$unit_totest)   # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT    nhat nhat.var NBRPLT NBRPLT.gt0 AREAUSED        est
    ## 1       1 1811.42  37356.8     37         34  1112412 2015045559
    ##             est.var    est.se    est.cv      pse   CI99left  CI99right
    ## 1 46227592716948680 215006029 0.1067003 10.67003 1461226728 2568864391
    ##     CI95left  CI95right   CI68left  CI68right
    ## 1 1593641485 2436449634 1801231118 2228860000

    head(raw4$unit_rowest)   # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT FORTYPCD       nhat   nhat.var NBRPLT NBRPLT.gt0
    ## 1       1      201   14.79053   183.9783     37          2
    ## 2       1      265  334.81975 28597.8813     37          4
    ## 3       1      266  255.54531 11338.6666     37          5
    ## 4       1      268   57.66920  1847.0547     37          2
    ## 5       1      281 1148.59483 38016.6026     37         23
    ## 6       1      901    0.00000     0.0000     37          0
    ##                        Forest type AREAUSED        est           est.var
    ## 1                      Douglas-fir  1112412   16453168   227665955699464
    ## 2                 Engelmann spruce  1112412  372457628 35388769702090616
    ## 3 Engelmann spruce / subalpine fir  1112412  284271758 14031160462237210
    ## 4                    Subalpine fir  1112412   64151931  2285658574567338
    ## 5                   Lodgepole pine  1112412 1277711074 47044072361108864
    ## 6                            Aspen  1112412          0                 0
    ##      est.se    est.cv      pse  CI99left  CI99right  CI95left  CI95right
    ## 1  15088604 0.9170637 91.70637         0   55318835         0   46026287
    ## 2 188119031 0.5050750 50.50750         0  857020140   3751103  741164153
    ## 3 118453199 0.4166900 41.66900         0  589386980  52107753  516435763
    ## 4  47808562 0.7452396 74.52396         0  187298626         0  157854990
    ## 5 216896455 0.1697539 16.97539 719022828 1836399319 852601833 1702820315
    ## 6         0       NaN      NaN         0          0         0          0
    ##     CI68left  CI68right
    ## 1    1448187   31458149
    ## 2  185381175  559534081
    ## 3  166475040  402068476
    ## 4   16608330  111695532
    ## 5 1062016684 1493405464
    ## 6          0          0

    head(raw4$unit_colest)   # estimates by column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT STDSZCD       nhat    nhat.var NBRPLT NBRPLT.gt0 Stand-size class
    ## 1       1       1 1312.13957 37720.24765     37         18   Large diameter
    ## 2       1       2  480.94618 16570.36181     37         14  Medium diameter
    ## 3       1       3   18.33387    71.08256     37          4   Small diameter
    ## 4       1       5    0.00000     0.00000     37          0       Nonstocked
    ##   AREAUSED        est           est.var    est.se    est.cv      pse  CI99left
    ## 1  1112412 1459640263 46677344541677816 216049403 0.1480155 14.80155 903133879
    ## 2  1112412  535010472 20505180519831456 143196301 0.2676514 26.76514 166161244
    ## 3  1112412   20394825    87961907913120   9378801 0.4598618 45.98618         0
    ## 4  1112412          0                 0         0       NaN      NaN         0
    ##    CI99right   CI95left  CI95right   CI68left  CI68right
    ## 1 2016146646 1036191214 1883089311 1244788231 1674492295
    ## 2  903859699  254350880  815670064  392607782  677413162
    ## 3   44553016    2012713   38776937   11068003   29721648
    ## 4          0          0          0          0          0

    head(raw4$unit_grpest)   # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ONEUNIT grpvar       nhat    nhat.var NBRPLT NBRPLT.gt0 FORTYPCD STDSZCD
    ## 1       1  201#1  11.884809   173.51852     37          1      201       1
    ## 2       1  201#3   2.905721    22.22926     37          1      201       3
    ## 3       1  265#1 252.436776 20564.47714     37          3      265       1
    ## 4       1  265#2  82.382977  9067.00974     37          1      265       2
    ## 5       1  266#1 232.253657 11575.99347     37          3      266       1
    ## 6       1  266#2  17.862348   172.20105     37          1      266       2
    ##   Stand-size class                      Forest type AREAUSED       est
    ## 1   Large diameter                      Douglas-fir  1112412  13220808
    ## 2   Small diameter                      Douglas-fir  1112412   3232360
    ## 3   Large diameter                 Engelmann spruce  1112412 280813787
    ## 4  Medium diameter                 Engelmann spruce  1112412  91643841
    ## 5   Large diameter Engelmann spruce / subalpine fir  1112412 258361838
    ## 6  Medium diameter Engelmann spruce / subalpine fir  1112412  19870297
    ##             est.var    est.se    est.cv       pse CI99left CI99right CI95left
    ## 1   214722440902215  14653411 1.1083597 110.83597        0  50965492        0
    ## 2    27507849841210   5244793 1.6225891 162.25891        0  16742051        0
    ## 3 25447743450416784 159523489 0.5680757  56.80757        0 691719064        0
    ## 4 11220073146973346 105924847 1.1558316 115.58316        0 364488165        0
    ## 5 14324843268872168 119686437 0.4632512  46.32512        0 566653670 23780731
    ## 6   213092123282188  14597675 0.7346481  73.46481        0  57471417        0
    ##   CI95right  CI68left CI68right
    ## 1  41940965         0  27793007
    ## 2  13511965         0   8448086
    ## 3 593474080 122174397 439453178
    ## 4 299252726         0 196981640
    ## 5 492942944 139338716 377384959
    ## 6  48481215   5353524  34387070

    # Titles (list object) for estimate
    titlelst4 <- tree4$titlelst
    names(titlelst4)

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"

    titlelst4

output

    ## $title.estpse
    ## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet (percent sampling error), by forest type and stand-size class on forest land"
    ##
    ## $title.yvar
    ## [1] "Net volume, in cubic feet"
    ##
    ## $title.estvar
    ## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia)"
    ##
    ## $title.unitvar
    ## [1] "ONEUNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet (percent sampling error), by forest type on forest land; Wyoming, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Net merchantable bole  wood volume of live trees (timber species at least 5 inch dia), in cubic feet (percent sampling error), by stand-size class on forest land; Wyoming, 2011-2013"
    ##
    ## $title.unit
    ## [1] "cubic feet"

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

output

    ## [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_modMA_mase_greg.csv"
    ## [2] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland.csv"

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

output

    ##  [1] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_domdat.csv"
    ##  [2] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_plotweights.csv"
    ##  [3] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_colest.csv"
    ##  [4] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_grpest.csv"
    ##  [5] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_rowest.csv"
    ##  [6] "WY_tree_VOLCFNET_FORTYPCD_STDSZCD_forestland_rawdata_modMA_mase_greg_unit_totest.csv"
    ##  [7] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_colest.csv"
    ##  [8] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_domdat.csv"
    ##  [9] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_grpest.csv"
    ## [10] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_rowest.csv"
    ## [11] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_totest.csv"
    ## [12] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_colest.csv"
    ## [13] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_grpest.csv"
    ## [14] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_rowest.csv"
    ## [15] "WY_tree_VOLCFNET_TPA_ADJ_live_FORTYPCD_STDSZCD_forestland_rawdata_unit_totest.csv"

#### Example 10: Number of live trees (plus seedlings) by species, Wyoming, 2011-2013

View Example

We can also add seedlings.

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

Note: must include seedling data in population data calculations.

    MApopdat_seed <- modMApop(popTabs = popTables(tree = WYtree,
                                                  cond = WYcond,
                                                  seed = WYseed),
                              pltassgn = WYpltassgn,
                              auxdat = modeldat)

    tree5 <- modMAtree(MApopdat = MApopdat_seed,              # pop - population calculations
                       MAmethod = "greg",                     # est - model-assisted method
                       prednames = c("dem", "slp", "fornf"),
                       estseed = "add",                       # est - add seedling data
                       landarea = "FOREST",                   # est - forest land filter
                       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(tree5)

output

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

    # Estimate and percent sampling error of estimate
    tree5$est

output

    ##                  Species     Estimate Percent Sampling Error
    ## 1          subalpine fir  790299214.1                  14.69
    ## 2 Rocky Mountain juniper           --                     --
    ## 3       Engelmann spruce  259528743.9                  20.03
    ## 4         lodgepole pine  311324152.7                  18.87
    ## 5            limber pine   41737320.1                 127.12
    ## 6            Douglas-fir   57576633.6                 102.22
    ## 7          quaking aspen   91703320.1                 107.42
    ## 8                  Other           --                     --
    ## 9                  Total 1552169384.5                  12.85