Significance Level for Random Forest Impurity Importance Scores

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Documentation for package ‘RFlocalfdr’ version 0.9

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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count_variables	count the number of times each variable is used in a ranger random forest
determine.C	determine.C
determine_cutoff	evaluate a measure that can be used to determining a significance level for the Mean Decrease in Impurity measure returned by a Random Forest model
dsn	my.dsn
f.fit	fit a spline to the histogram of imp
fit.to.data.set	fit.to.data.set
fit.to.data.set.wrapper	fit.to.data.set.wrapper
imp20000	20000 importance values
local.fdr	local fdr
my.dsn	my.dsn
my.test1fun	my.test1fun
my_PIMP	my_PIMP based on the PIMP function from the vita package. <e2><80><98>PIMP<e2><80><99> implements the test approach of Altmann et al. (2010) for the permutation variable importance measure <e2><80><98>VarImp<e2><80><99> returned by the randomForest package (Liaw and Wiener (2002)) for classification and regression.
my_ranger_PIMP	my_ranger_PIMP based on the PIMP function from the vita package. <e2><80><98>PIMP<e2><80><99> implements the test approach of Altmann et al. (2010) for the permutation variable importance measure <e2><80><98>VarImp<e2><80><99> returned by the randomForest package (Liaw and Wiener (2002)) for classification and regression.
plotQ	plotQ
propTrueNullByLocalFDR	propTrueNullByLocalFDR
psn	my.dsn
qsn	my.dsn
run.it.importances	run.it.importances
significant.genes	significant.genes