Estimating the variance covariance components matrix under the multivariate mixed effects model. Currently this package supports multivariate mixed effects model with two response variables, one fixed effects and one random effects.
Estimation MethodsTo install from CRAN:
install.packages("MMeM")
library(MMeM)
You can also use devtools to install the latest development version:
devtools::install_github("pengluyaoyao/MMeM")
library(MMeM)
Examples
library(MMeM)
data(simdata)
T.start = matrix(c(10,5,5,15),2,2)
E.start = matrix(c(10,1,1,3),2,2)
results_henderson = MMeM_henderson3(fml = c(V1,V2) ~ X_vec + (1|Z_vec), data = simdata, factor_X = TRUE)
results_reml = MMeM_reml(fml = c(V1,V2) ~ X_vec + (1|Z_vec), data = simdata, factor_X = TRUE, T.start = T.start, E.start = E.start, maxit = 10)
# using lme4 to analyze univariate mixed effects model:
alcohol1 <- read.table("https://stats.idre.ucla.edu/stat/r/examples/alda/data/alcohol1_pp.txt", header=T, sep=",")
attach(alcohol1)
mod1<-lme4::lmer(alcuse ~ age +(1|id) ,alcohol1,REML=1)
summary(mod1)
library(merDeriv)
vcov(mod1, full =TRUE)
# Compare with lme4:
T.start = 3
E.start = 4
results = MMeM_reml(alcuse ~ age + (1|id), alcohol1, factor_X = FALSE, T.start, E.start)
Values
MMeM_reml: - T.estimates: the estimated matrix of the variance covariance matrix of the block random effects - E.estimates is the estimated matrix of the variance covariance matrix of the residuals - VCOV is the asymptotic dispersion matrix of the estimated variance covariance components
MMeM_henderson3: - T.estimates: the estimated matrix of the variance covariance matrix of the block random effects with corresponding standard errors - E.estimates is the estimated matrix of the variance covariance matrix of the residuals with corresponding standard errors
ReferencesMeyer, K. A. R. I. N. âMaximum likelihood estimation of variance components for a multivariate mixed model with equal design matrices.â Biometrics 1985: 153-165
WesolowskaâJanczarek, M. T. âEstimation of covariance matrices in unbalanced random and mixed multivariate models.â Biometrical journal 26.6 (1984): 665-674.
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