Cross-Validated R^2 value

Computes an R^2 value for predicting an outcome measure using a k-fold cross-validation scheme.

crossvalidatedR2(x, y, ngroups=5, covariates=NA, fast=F)

Arguments

x	Input predictor matrix.
y	Target dependent variable.
ngroups	Number of cross-validation folds to use or the fold labels themselves, equal to the length of y. e.g. c(1,1,1,2,2,2...)
covariates	Covariate predictors.
fast	Use low-level lm.fit instead of lm. Much faster, but less error checking.

Value

Matrix of size ngroups by ncol(x), which each row corresponding to one fold and the columns corresponding to the R2 values for each predictor.

Examples

set.seed(300)
ncol <- 30
nrow <- 20
covariate <- sin((1:nrow)*2*pi/nrow)
x <- matrix(rep(NA, nrow*ncol), nrow=nrow)
xsig <- seq(0,1,length.out=nrow)
y <- xsig + covariate + rnorm(nrow, sd=0.5)
for(i in 1:ncol){
  x[, i] <- xsig + rnorm(nrow, sd=i/ncol)
}
r2 <- crossvalidatedR2(x, y, covariates=covariate)