MMeM (Multivariate Mixed-effects Model)

Description

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 Methods

Installation

To 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:

MMeM_henderson3:

References

Meyer, K. A. R. I. N. "Maximum likelihood estimation of variance components for a multivariate mixed model with equal design matrices." Biometrics 1985: 153-165

Wesolowska‐Janczarek, M. T. "Estimation of covariance matrices in unbalanced random and mixed multivariate models." Biometrical journal 26.6 (1984): 665-674.