lmerUvcov {lme4GS}R Documentation

Fits a linear mixed model with user specified variance covariance-matrices.

Description

Fits a linear mixed model with user specified variance covariance-matrices.

Usage

  lmerUvcov(formula, data = NULL, Uvcov = NULL,verbose=0L)

Arguments

formula

a two-sided linear formula object describing both the fixed-effects and random-effects part of the model, with the response on the left of a ‘~’ operator and the terms, separated by ‘+’ operators, on the right. Random-effects terms are distinguished by vertical bars (‘|’) separating expressions for design matrices from grouping factors.

data

an optional data frame containing the variables named in ‘formula’.

Uvcov

list.

verbose

integer scalar, verbose output from optimizeLmer function?. If '> 0' verbose output is generated during the optimization of the parameter estimates, default value is 0L.

Details

The routine fits the linear mixed model:

y=Xβ + Z1 u1 + ... + Zq uq + e,

where \boldsymbol y is the response vector, \boldsymbol X is the matrix for fixed effects, β is the vector of fixed effects, Zj is a design matrix for random effects, uj is a vector of random effects, j=1,\dots,q. We assume that uj~N(0,σ2j K j), j=1,\dots,q and e~N(0,σ2eI).

The linear mixed model can be re-written as:

y=Xβ + Z1* u1*+...+Zq* uq*+e,

where Zj*=Zj Lj, with Lj from Cholesky factorization for Kj. Alternatively, Zj*=ZjΓjΛ1/2, with Γj and Λj the matrix of eigen-vectors and eigen-values obtained from the eigen-value decomposition for Kj. The factorization method for Kj is selected automatically at runtime.

Value

An object of class merMod (more specifically, an object of subclass lmerMod), for which many methods are available (e.g. methods(class="merMod"))

Author(s)

Paulino Perez-Rodriguez

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




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)




[Package lme4GS version 0.1 Index]