get.lmer.effects: extract results
In eatGet: eatGet

Description Usage Arguments Value Author(s) Examples

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

1	get.lmer.effects ( lmerObj , bootMerObj = NULL, conf = .95, saveData = FALSE )

`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 attribut?

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

Note: 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.

Sebastian Weirich

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)