Description Usage Arguments Value Author(s) Examples
View source: R/crossvalidatedR2.R
Computes an R^2 value for predicting an outcome measure using a k-fold cross-validation scheme.
1 | crossvalidatedR2(x, y, ngroups=5, covariates=NA, fast=F)
|
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 |
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.
Brian B Avants, Benjamin M. Kandel
1 2 3 4 5 6 7 8 9 10 11 | 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)
|
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