Significance Level for Random Forest Impurity Importance Scores


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Documentation for package ‘RFlocalfdr’ version 0.9

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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