get.lmer.effects: Extract results

In beckerbenj/eatAnalysis: Convenience function for various analysis types

Extract results

Description

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?

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.

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

Author(s)

Sebastian Weirich

Examples

## Not run: 
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)

## End(Not run)

beckerbenj/eatAnalysis documentation built on July 7, 2023, 5:51 p.m.