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

## Description

Calculates the R2 of a cluster of SNPs.

## Usage

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

## Arguments

 `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.

## Details

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.

## Value

The R2 value of the SNP cluster

## References

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 (unpublished)

## Examples

 ```1 2 3 4 5 6 7 8``` ```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) ```

### Example output

```
```

hierGWAS documentation built on Nov. 8, 2020, 8:05 p.m.