lme4GS (version 0.1)

ranefUvcov: Extract the conditional means of the random effects

Description

A function to extract the conditional means of the random effects from a fitted model object.

Usage

ranefUvcov(object, postVar = FALSE, drop = FALSE, whichel = names(ans), ...)

Value

A list of data frames, one for each grouping factor.

Arguments

object

is an object returned by lmerUvcov.

postVar

a logical argument indicating if the conditional variance-covariance matrices of the random effects should be added as an attribute.

drop

should components of the return value that would be data frames with a single column, usually a column called ``(Intercept)'', be returned as named vectors instead?.

whichel

character vector of names of grouping factors for which the random effects should be returned.

...

some methods for these generic functions require additional arguments.

Author

Paulino Perez-Rodriguez

Details

The function ranef extract the conditional means for the liner mixed effects model:

y=Xβ + Z₁^* u₁^*+...+Z_q^* u_q^*+e,

where Z_j^*=Z_j L_j, with L_j from Cholesky factorization for K_j. Alternatively, Z_j^*=Z_jΓ_jΛ^1/2, with Γ_j and Λ_j the matrix of eigen-vectors and eigen-values obtained from the eigen-value decomposition for K_j. So, the conditional means of the random effects in the linear mixed effects model:

y=Xβ + Z₁ u₁ + ... + Z_q u_q + e,

are obtained as follows: û_j=L_jû_j^* if the Cholesky factorization is used or û_j=Γ_jΛ_j^1/2û_j^* if the the eigen-value decomposition is used.

References

Caamal-Pat D., P. Perez-Rodriguez, J. Crossa, C. Velasco-Cruz, S. Perez-Elizalde, M. Vazquez-Pena. 2021. lme4GS: An R-Package for Genomic Selection. Front. Genet. 12:680569. doi: 10.3389/fgene.2021.680569 doi: 10.3389/fgene.2021.680569

Examples

# \donttest{

library(BGLR)
library(lme4GS)

########################################################################
#Example wheat
########################################################################
data(wheat)
X<-wheat.X
Z<-scale(X,center=TRUE,scale=TRUE)
G<-tcrossprod(Z)/ncol(Z)
A<-wheat.A
rownames(G)<-colnames(G)<-rownames(A)
y<-wheat.Y[,1]

data<-data.frame(y=y,m_id=rownames(G),a_id=rownames(A))

fm1<-lmerUvcov(y~(1|m_id)+(1|a_id),data=data,
               Uvcov=list(m_id=list(K=G),a_id=list(K=A)))

summary(fm1)

#Predictions
plot(y,predict(fm1))

#Random effects
ranef(fm1)

#Equivalently
ranefUvcov(fm1)

# }