compute.r2: R2 computation
In hierGWAS: Asessing statistical significance in predictive GWA studies

Description Usage Arguments Details Value References Examples

View source: R/compute.r2.R

Calculates the R2 of a cluster of SNPs.

1	compute.r2(x, y, res.multisplit, covar = NULL, SNP_index = NULL)

`x`	The input matrix, of dimension `nobs x nvar`. Each row represents a subject, each column a SNP.
`y`	The response vector. It can be continuous or discrete.
`res.multisplit`	The output of `multisplit`.
`covar`	`NULL` or the matrix of covariates one wishes to control for, of size `nobs x ncovar`.
`SNP_index`	`NULL` or the index vector of the cluster of SNPs whose R2 will be computed. See the 'Details' section.

The R2 of a cluster of SNPs is computed on the second half-samples. The cluster members, are intersected with the SNPs selected by the lasso, and the R2 of this model is calculated. Thus, we obtain B R2 values. Finally, the mean of these values is taken. If the value of SNP_index is NULL, the R2 of the full model with all the SNPs will be computed.

The R2 value of the SNP cluster

Buzdugan, L. et al. (2015), Assessing statistical significance in predictive genome-wide association studies. (unpublished)

library(MASS)
x <- mvrnorm(60,mu = rep(0,60), Sigma = diag(60))
beta <- rep(0,60)
beta[c(5,9,3)] <- 1
y <- x %*% beta + rnorm(60)
SNP_index <- c(5,9,3)
res.multisplit <- multisplit(x, y)
r2 <- compute.r2(x, y, res.multisplit, SNP_index = SNP_index)