Selvar {EstHer} R Documentation

## Estimation of heritability in high dimensional sparse linear mixed models using variable selection.

### Description

This function selects active components in sparse linear mixed models in order to estimate heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.

### Usage

Selvar(Y,Z,X,thresh_vect,nb_boot=80,nb_repli=50,CI_level=0.95,nb_cores=1)


### Arguments

 Y Vector of observations of size n. Z Matrix with genetic information of size n x N. X Matrix of fixed effects of size n x d. thresh_vect Vector of thresholds in the stability selection step: the higher the threshold, the smallest the set of selected components. nb_boot Number of subsamples of Y to apply our bootstrap technique. The value by default is 80. nb_repli Number of replications in the stability selection. The value by default is 50. CI_level Level of the confidence interval for the estimation of the heritability. The value by default is 0.95. nb_cores Number of cores of the computer. It is used for parallelizing the computations. The value by default is 1.

### Value

 heritability Estimation of the heritability CI_up Upper bound of the confidence interval for the estimated heritability CI_low Lower bound of the confidence interval for the estimated heritability selec_ind Indexes of the columns of the selected components

### Author(s)

Anna Bonnet and Celine Levy-Leduc

### Examples

library(EstHer)
data(Y)
data(W)
data(X)
Z=scale(W,center=TRUE,scale=TRUE)
res=Selvar(Y,Z,X,thresh_vect=c(0.7,0.8,0.9),nb_boot=80,nb_repli=50,CI_level=0.95,nb_cores=1)
res$heritability res$CI_low
res\$CI_up


[Package EstHer version 1.0 Index]