Description Usage Arguments Details Value Author(s) References Examples
Residuals from the best linear unbiased predictor of the genetic effects (GBLUP), which is computed given REML-estimates of the variance components.
1 | getResiduals(suffStat, covariates = NULL, cov.method = "uni", K = NULL)
|
suffStat |
A data.frame, of which the first column is the factor G (genotype), and subsequent columns contain the traits. The name of the first column should be G. |
covariates |
A data.frame containing covariates, that should always be used in each conditional independence test. Should be either |
cov.method |
(A string, specifying which method should be used to compute the GBLUP. Options are |
K |
A genetic relatedness matrix. If |
If cov.method = "uni"
, the GBLUP and the residuals are computed separately for each trait in suffStat. The covariance of each trait is then assumed to be
σ_G^2 Z K Z^t + σ_E^2 I_n
where Z is a binary incidence matrix, assigning plants or plots to genotypes. Z is based on the first column in suffStat
. If there is a single observation per genotype (typically a genotypic mean), Z is the identity matrix, and the relatedness matrix K should be specified. If there are replicates for at least some of the genotypes, and no K is provided, independent genetic effects are assumed (K will be the identity matrix). It is also possible to have replicates and specify a non-diagonal K.
Whenever K is specified, sommer (mmer2) will be used; otherwise lmer (lme4). The mmer2 is also used when cov.method = "us"
, in which case the multivariate GBLUP is computed, for all traits in suffStat
simultaneously. This is only possible for a limited number of traits.
A data-frame with the residuals.
Willem Kruijer and Pariya Behrouzi. Maintainers: Willem Kruijer willem.kruijer@wur.nl and Pariya Behrouzi pariya.behrouzi@gmail.com
Covarrubias-Pazaran, G., 2016. Genome-assisted prediction of quantitative traits using the R package sommer. PloS one, 11(6), p.e0156744.
1 2 | data(simdata)
rs <- getResiduals(suffStat= simdata)
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