bigRR (version 1.3-10)

bigRR_update: Updating a bigRR fit to be a heteroscedastic effects model (HEM) fit

Description

This function updates the obtained bigRR object into a new object with heteroscedasticity assumption.

Usage

bigRR_update(obj, Z, family = gaussian(link = identity),  tol.err = 1e-6, tol.conv = 1e-8, GPU = FALSE)

Arguments

obj

A bigRR object.

Z

The design matrix for the shrinkage/random effects.

family

the distribution family of y, see help('family') for more details.

tol.err

internal tolerance level for extremely small values; default value is 1e-6.

tol.conv

tolerance level in convergence; default value is 1e-8.

GPU

logical; specify whether GPU should be used in computation. Note that: 1. this option is only available in the R-Forge versions of bigRR; 2. the package gputools is required in this case, and the computer's graphic card needs to be CUDA-enabled. Check e.g. NVIDIA website for more information.

Details

See the reference paper for details.

References

Shen X, Alam M, Fikse F and Ronnegard L (2013). A novel generalized ridge regression method for quantitative genetics. Genetics, 193, 1255-1268.

Examples

# --------------------------------------------- #  
#              Arabidopsis example              #
# --------------------------------------------- #  

require(bigRR)
data(Arabidopsis)
X <- matrix(1, length(y), 1)

## Not run: 
# # fitting SNP-BLUP, i.e. a ridge regression on all the markers across the genome
# #
# SNP.BLUP.result <- bigRR(y = y, X = X, Z = scale(Z), 
#                          family = binomial(link = 'logit'))
# 
# # fitting HEM, i.e. a generalized ridge regression with marker-specific shrinkage
# #
# HEM.result <- bigRR_update(SNP.BLUP.result, scale(Z), 
#                            family = binomial(link = 'logit'))
# 
# # plot and compare the estimated effects from both methods
# #
# split.screen(c(1, 2))
# split.screen(c(2, 1), screen = 1)
# screen(3); plot(abs(SNP.BLUP.result$u), cex = .6, col = 'slateblue')
# screen(4); plot(abs(HEM.result$u), cex = .6, col = 'olivedrab')
# screen(2); plot(abs(SNP.BLUP.result$u), abs(HEM.result$u), cex = .6, pch = 19, 
#                 col = 'darkmagenta')
# 
# # create a random new genotypes for 10 individuals with the same number of markers 
# # and predict the outcome using the fitted HEM
# #
# Z.new <- matrix(sample(c(-1, 1), 10*ncol(Z), TRUE), 10)
# y.predict <- as.numeric(HEM.result$beta + Z.new %*% HEM.result$u)
# #
# # NOTE: The above prediction may not be good due to the scaling in the HEM 
# #       fitting above, and alternatively, one can either remove the scaling 
# #       above or scale Z.new by row-binding it with the original Z matrix.
# ## End(Not run)