summaryExp: Generalized Linear Mixed Effect Model Summary with...

In sfraundorf/psycholing: R Functions for Common Psycholingusitic and Cognitive Designs

Generalized Linear Mixed Effect Model Summary with Exponeniated Coefficients

Description

For a glm or glmer model fit using the logit or log link function, displays a model summary with the exponentiation function applied to the fixed effect point estimates. Thus, estimates of effects on the log odds in a logistic model become estimates of effects on the odds, which are usually more interpretable. Similarly, estimates of effects on the log counts in a Poisson regression become estimate of effcts on the counts to facilitate interpretation.

Usage

summaryExp(model, confidence = 0.95)

Arguments

model	glmer model to be summarized.
confidence	desired level of confidence for the confidence interval.

Details

Note that once the coefficients have been exponeniated, the estimates now represent multiplicative effects. For instance, a variable associated with an odds ratio of 1.25 in a logistic regression means the variable is associated with 1.25 TIMES the odds ofsuccess. (This is different from regular linear regression, where the effects are additive.)

Consequently, a factor that has no effect on the response variable would have an estimate of 1 rather than 0. (Multiplying something by 1 leaves it unchanged, of course.)

95 effects are multiplicative, the CIs will NOT be symmetric about the point estimate on the original scale. Thus, it would be preferable to report the lower- and upper-bound of the CI (which is the current APA style for confidence intervals anyway) rather than its width.

Value

model summary including exponentiated fixed effect estimates and confidence intervals.

Examples

data(VerbAgg, package='lme4')
model1 <- glmer(r2 ~ Gender + btype+ (1|id) + (1|item),
  family=binomial, data=VerbAgg)
summaryExp(model1)

sfraundorf/psycholing documentation built on April 23, 2022, 2:50 a.m.