bigRR_update: Updating a bigRR fit to be a heteroscedastic effects model...

In bigRR: Generalized Ridge Regression (with special advantage for p >> n cases)

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 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.

Author(s)

Xia Shen, Lars Ronnegard

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              #
# --------------------------------------------- #  
## Not run: 
require(bigRR)
data(Arabidopsis)
X <- matrix(1, length(y), 1)

# 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)

bigRR documentation built on July 25, 2020, 3 a.m.