R: Significance Level for Random Forest Impurity Importance Scores

# Significance Level for Random Forest Impurity Importance Scores

## Documentation for package ‘RFlocalfdr’ version 0.9

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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. ‘PIMP’ implements the test approach of Altmann et al. (2010) for the permutation variable importance measure ‘VarImp’ 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. ‘PIMP’ implements the test approach of Altmann et al. (2010) for the permutation variable importance measure ‘VarImp’ 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