loglikfuncmmmkmv: Calculate the loglikeligood for a general mixed model

In SAMM: Some Algorithms for Mixed Models

Description

Calculate the loglikeligood for the LMM model expressed as

Y=XB+∑_{j=1}^k Z_j G_j+WE,

where Y is the n \times d response variable, X is the n \times q design matrix of q \times d the fixed effects B, Z_j for j=1,2,…,k (k≥q 1) are the n \times q_j design matrices of the q_j \times d random effects G_j, and W is the n \times t design matrix of t \times d residual effecs E. The random effects and the residual are independently distributed, and have matrix variate distributions (G_j\sim N_{q_j \times d}(0_{q_j \times d}, K_j,Σ_j) for j=1,2,…,k and E\sim N_{t \times d}(0_{t \times d}, R,Σ_E)).

Usage

loglikfuncmmmkmv(Y,X,Zlist, Klist, sigmahatlist, B,W,R )

Arguments

Y	a numeric vector for the parameters (a mapping of the original parameters)
X	a numeric matrix (see examples and details)
Zlist	a numeric matrix (see examples and details)
Klist	a numeric matrix (see examples and details)
sigmahatlist	a numeric matrix (see examples and details)
B	bla bla
W	bla bal
R	bla bla

Details

bla bla

Value

bla bla

Author(s)

Deniz Akdemir

Examples

## Not run: 
library(SAMM)
n=100
nsample=80
rhotrans=5
ar1cov_cppforR(c(rhotrans),matrix(5))
rho=(2/pi)*atan(rhotrans)
rho
tan((pi/2)*(rho))

M1<-matrix(rbinom(n*300, 2, .2)-1, nrow=n)
K1<-relmatcov_cppforR(c(.01), M1)

M2<-matrix(rbinom(n*300, 2, .2)-1, nrow=n)
K2<-relmatcov_cppforR(c(0.03), M2)
W=(diag(5)[sample(1:5,n, replace=TRUE),])
covY<-3*K1+5*K2+10*(W%*%ar1cov_cppforR(c(rhotrans),matrix(5))%*%t(W))
K1[1:5,1:5]
dim(W)
dim(ar1cov_cppforR(c(6),matrix(5)))
Y<-10+crossprod(chol(covY),rnorm(n))


#training set
Trainset<-sample(1:n,nsample)
ytrain=Y[Trainset]
Xtrain=matrix(rep(1, n)[Trainset], ncol=1)
Ztrain=diag(n)[Trainset,]
Wtrain=W[Trainset,]

samout<-SAMM(Y=matrix(ytrain,ncol=1),X=Xtrain,
Zlist=list(Ztrain, Ztrain), Klist=list(K1,K2),
lambda=0, W=Wtrain,R=list("ar1",c(0),matrix(5,1,1)),
Siglist=list("","",""), corfunc=c(F,F,T), corfuncfixed=c(F,F,F),
sigfunc=c(F,F,F),mmalg="dermm_reml2", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout$corfuncparamslist[[3]]
rhohat=(2/pi)*atan(samout$corfuncparamslist[[3]])
rhohat
ar1cov_cppforR(c(samout$corfuncparamslist[[3]]),matrix(5,1,1))

## End(Not run)

SAMM documentation built on May 2, 2019, 2:08 a.m.