# Statistical Inference for Fixed and Random Structure for Fitted Models in `lme4` 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. ```r lmer_vcov(object, level=.95, use_reml=FALSE, ...) ## S3 method for class 'lmer_vcov' summary(object, digits=4, file=NULL, ...) ## S3 method for class 'lmer_vcov' coef(object, ...) ## S3 method for class 'lmer_vcov' vcov(object, ...) lmer_vcov2(object, level=.95, ...) lmer_pool( models, level=.95, ...) ## S3 method for class 'lmer_pool' summary(object, digits=4, file=NULL, ...) lmer_pool2( models, level=.95, ...) ``` ## 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 ## Returns List with several entries: - **par_summary**: Parameter summary - **coef**: Estimated parameters - **vcov**: Covariance matrix of estimates - ****: Further values ## Author(s) Function originally from Ben Bolker, http://rpubs.com/bbolker/varwald ## See Also `lme4::lmer`, `mitml::testEstimates` ## Examples ```r ## Not run: ############################################################################# # 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) ## End(Not run) ```

lmer_vcov function