lmer_vcov: Statistical Inference for Fixed and Random Structure for Fitted Models in lme4

Description

The function lmer_vcov conducts statistical inference for fixed coefficients and standard deviations and correlations of random effects structure of models fitted in the lme4 package.

The function lmer_pool applies the Rubin formula for inference for fitted lme4 models for multiply imputed datasets.

Usage

lmer_vcov(object, level=.95, use_reml=FALSE, ...)
# S3 method for lmer_vcov
summary(object, digits=4, file=NULL, ...)
# S3 method for lmer_vcov
coef(object, ...)
# S3 method for lmer_vcov
vcov(object, ...)
lmer_vcov2(object, level=.95, ...)
lmer_pool( models, level=.95, ...)
# S3 method for lmer_pool
summary(object, digits=4, file=NULL, ...)
lmer_pool2( models, level=.95, ...)

Value

List with several entries:

par_summary: Parameter summary
coef: Estimated parameters
vcov: Covariance matrix of estimates
...: Further values

Arguments

object: Fitted object in lme4
level: Confidence level
use_reml: Logical indicating whether REML estimates should be used for variance components (if provided)
digits: Number of digits used for rounding in summary
file: Optional file name for sinking output
models: List of models fitted in lme4 for a multiply imputed dataset
...: Further arguments to be passed

Author

Function originally from Ben Bolker, http://rpubs.com/bbolker/varwald

Examples

Run this code

if (FALSE) {
#############################################################################
# EXAMPLE 1: Single model fitted in lme4
#############################################################################

library(lme4)
data(data.ma01, package="miceadds")
dat <- na.omit(data.ma01)

#* fit multilevel model
formula <- math ~ hisei + miceadds::gm( books, idschool ) + ( 1 + books | idschool )
mod1 <- lme4::lmer( formula, data=dat, REML=FALSE)
summary(mod1)

#* statistical inference
res1 <- miceadds::lmer_vcov( mod1 )
summary(res1)
coef(res1)
vcov(res1)

#############################################################################
# EXAMPLE 2: lme4 model for multiply imputed dataset
#############################################################################

library(lme4)
data(data.ma02, package="miceadds")
datlist <- miceadds::datlist_create(data.ma02)

#** fit lme4 model for all imputed datasets
formula <- math ~ hisei + miceadds::gm( books, idschool ) + ( 1 | idschool )
models <- list()
M <- length(datlist)
for (mm in 1:M){
    models[[mm]] <- lme4::lmer( formula, data=datlist[[mm]], REML=FALSE)
}

#** statistical inference
res1 <- miceadds::lmer_pool(models)
summary(res1)
}