estEffsCPP | R Documentation |
Compute the estimates and standard errors for the covariates in the input matrix W.
estEffsCPP(
y0,
w0,
x0,
vg,
ve,
k,
returnSe = TRUE,
estCom = FALSE,
nCores = NULL
)
y0 |
An n x p matrix of observed phenotypes, on p traits or environments for n genotypes. No missing values are allowed. |
w0 |
An n x c covariate matrix, c being the number of covariates and n being the number of genotypes. c has to be at least one (typically an intercept). No missing values are allowed. |
x0 |
An n x ns matrix of marker scores. Neither missing values nor non-segregating markers are allowed. |
vg |
A p x p matrix of genetic covariances. |
ve |
A p x p matrix of environmental covariances. |
k |
An n x n genetic relatedness matrix. |
returnSe |
Should standard errors and p-values be returned? |
estCom |
Should the common SNP-effect model be fitted? |
nCores |
An integer indicating the number of cores used for parallel computation. |
A list containing the estimates, optionally the standard errors of
the estimates and corresponding p-values. If estCom = TRUE
also
common SNP-effects, their standard errors and corresponding p-values and
the p-values for QtlxE are output.
Zhou, X. and Stephens, M. (2014). Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature Methods, February 2014, Vol. 11, p. 407–409
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