cov.sel.high.rf: cov.sel.high.rf
In CovSelHigh: Model-Free Covariate Selection in High Dimensions

Description Usage Arguments Details Value Author(s) See Also

Function called by cov.sel.high if type="rf". Not meant to be used on its own.

1	cov.sel.high.rf(Y, X, threshold = 0.25, ntree = 1000, ...)

`Y`	Outcome variable or treatment variable.
`X`	A matrix or data frame containing columns of covariates.
`threshold`	Variable importance threshold, see Value.
`ntree`	Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
`...`	Additional arguments passed on to `randomForest`.

See randomForest.

cov.sel.high.rf returns a logical vector of the same length as the number of columns in X. The positions of values in the vector refers to the covariates in the corresponding X columns. Value TRUE implies that the covariate has a variable importance value of more than threshold*the largest observed variable importance value.

Jenny Häggström, <jenny.haggstrom@umu.se>

randomForest

CovSelHigh documentation built on May 2, 2019, 3:25 a.m.