vimp.rfcca: Variable importance for rfcca objects

In RFCCA: Random Forest with Canonical Correlation Analysis

Variable importance for rfcca objects

Description

Calculates variable importance measures (VIMP) for subject-related z-variables for training data.

Usage

## S3 method for class 'rfcca'
vimp(object, ...)

Arguments

object	An object of class (rfcca,grow).
...	Optional arguments to be passed to other methods.

Value

An object of class (rfcca,predict) which is a list with the following components:

call	The original grow call to rfcca.
n	Sample size of the data (NA's are omitted).
ntree	Number of trees grown.
zvar	Data frame of z-variables.
zvar.names	A character vector of the z-variable names.
predicted.oob	OOB predicted canonical correlations for training observations based on the selected final canonical correlation estimation method.
finalcca	The selected CCA used for final canonical correlation estimations.
importance	Variable importance measures (VIMP) for each z-variable.

See Also

plot.vimp.rfcca

Examples

## load generated example data
data(data, package = "RFCCA")
set.seed(2345)

## train rfcca
rfcca.obj <- rfcca(X = data$X, Y = data$Y, Z = data$Z, ntree = 100)

## get variable importance measures
vimp.obj <- vimp(rfcca.obj)
vimp.z <- vimp.obj$importance

RFCCA documentation built on Sept. 19, 2023, 9:06 a.m.