GSelection (version 0.1.0)

spam.var.rcv: Error Variance Estimation in Genomic Prediction

Description

Estimation of error variance using Refitted cross validation in Sparse Additive Models.

Usage

spam.var.rcv(x,y,d)

Arguments

x

a matrix of markers or explanatory variables, each column contains one marker and each row represents an individual.

y

a column vector of response variable.

d

number of variables to be selected from x.

Value

Error variance

Details

Refitted cross validation method (RCV) which is a two step method, is used to get the estimate of the error variance. In first step, dataset is divided into two sub-datasets and with the help of Sparse Additive Models (SpAM) most significant markers(variables) are selected from the two sub-datasets. This results in two small sets of selected variables. Then using the set selected from 1st sub-dataset error variance is estimated from the 2nd sub-dataset with ordinary least square method and using the set selected from the 2nd sub-dataset error variance is estimated from the 1st sub-dataset with ordinary least square method. Finally the average of those two error variances are taken as the final estimator of error variance with RCV method.

References

Fan, J., Guo, S., Hao, N. (2012).Variance estimation using refitted cross-validation in ultrahigh dimensional regression. Journal of the Royal Statistical Society, 74(1), 37-65. Ravikumar, P., Lafferty, J., Liu, H. and Wasserman, L. (2009). Sparse additive models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(5), 1009-1030. doi:10.1111/j.1467-9868.2009.00718.x

Examples

# NOT RUN {
library(GSelection)
data(GS)
x_trn <- GS[1:40,1:110]
y_trn <- GS[1:40,111]
x_tst <- GS[41:60,1:110]
y_tst <- GS[41:60,111]
spam_var <- spam.var.rcv(x_trn,y_trn,10)
# }