metahdep.other {metahdep} | R Documentation |
Miscellaneous functions used internally by the metahdep package's main functions (metahdep
, metahdep.FEMA
, metahdep.REMA
, metahdep.HBLM
, and metahdep.format
):
metahdep.list2dataframe | convert list to data.frame |
LinMod.MetAn.dep.REMA | REMA meta-analysis |
LinMod.REMA.dep | used by LinMod.MetAn.dep.REMA to estimate parameters |
LinMod.REMA.delta.split | REMA (with delta-splitting) |
LinMod.HBLM.fast.dep | HBLM (no delta-splitting) |
new.LinMod.HBLM.fast.dep.delta.split | HBLM (with delta-splitting) |
LinMod.MetAn.dep.FEMA | FEMA |
metahdep.check.X | check design matrix X, and drop columns if necessary |
to make full rank | |
get.M | create block diagonal M matrix, given dependence structure |
tr | calculate trace of matrix |
id | create identity matrix |
center.columns | center all non-intercept columns of design matrix X |
mod | mod function |
get.varsigma.v | get varsigma values for HBLM delta-splitting model |
John R. Stevens, Gabriel Nicholas
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
Stevens J.R. and Taylor A.M. (2009), Hierarchical Dependence in Meta-Analysis, Journal of Educational and Behavioral Statistics, 34(1):46-73.
See also the metahdep package vignette.
## Create the M matrix for the glossing example ## - here, studies 2-5 are one hierarchically dependent group (Baumann), ## and studies 10-12 are another hierarchically dependent group (Joyce) data(gloss) dep.groups <- list(c(2:5),c(10:12)) M <- get.M(length(gloss.theta),dep.groups)