Description Usage Arguments Details Author(s) References Examples
This function updates the obtained bigRR object into a new object with heteroscedasticity assumption.
1 2 |
obj |
A |
Z |
The design matrix for the shrinkage/random effects. |
family |
the distribution family of |
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 |
See the reference paper for details.
Xia Shen, Lars Ronnegard
Shen X, Alam M, Fikse F and Ronnegard L (2013). A novel generalized ridge regression method for quantitative genetics. Genetics, 193, 1255-1268.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | # --------------------------------------------- #
# 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)
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