Extract results — get.lmer.effects • eatAnalysis

Extract results from an object created by lmer or glmer from the lme4 package.

Usage

get.lmer.effects(lmerObj, bootMerObj = NULL, conf = 0.95, saveData = FALSE)

Arguments

lmerObj: An object of class merMod or glmerMod, as created by lmer or glmer from the lme4 package.
bootMerObj: Optional: An object of S3 class boot, as created by bootMer. Applies if standard error and/or confidence intervals from a bootstrap should be augmented to the lme4 results object.
conf: Applies if confidence intervals from a bootstrap should be augmented to the lme4 results object. Define the upper bound of the confidence interval.
saveData: Logical: Should the data frame be attached to the output as an attribute?

Value

A data frame with at least 10 columns comprising the results of the GLMM analysis.

model: The name of the object the analysis results are assigned to.
source: The lmer-function called
var1: First variable name
var2: Second variable name
type: Type of variable and/or derived parameter
group: The group a model parameter belongs to
par: Name of the model parameter
derived.par: Second name of the model parameter
var2: Second variable name
value: Corresponding numerical value

Details

In principle, get.lmer.effects collects only output already contained in the lme4-output. Additionally, the marginal and conditional r-squared from Nakagawa and Schielzeth (2013) is provided. The parameters are labeled R2_m and R2_c in the par-column.

Author

Sebastian Weirich

Examples

if (FALSE) { # \dontrun{
library ( lme4 )
### First example: GLMM analysis
fmVA <- glmer( r2 ~ Anger + Gender + btype + situ + (1|id) + (1|item),
               family = binomial, data = VerbAgg)
results    <- get.lmer.effects ( fmVA )

### second example: obtain standard errors and confidence intervals from the model estimated
### in the first example via bootstrap (using only 5 bootstrap samples for illustration)
### We use the 'bootMer' function fom the lme4 package
fmVAB<- bootMer(x = fmVA, FUN = get.lmer.effects.forBootMer, nsim = 5)
resultsBoot<- get.lmer.effects ( lmerObj = fmVA, bootMerObj = fmVAB, conf = .95, saveData = FALSE)
} # }