glmer.mp.con {multpois}R Documentation

Contrast tests for multinomial-Poisson GLMMs

Description

This function conducts post hoc pairwise comparisons on generalized linear mixed models (GLMMs) built with glmer.mp. Such models have nominal response types, i.e., factors with unordered categories.

Usage

glmer.mp.con(
  model,
  formula,
  adjust = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"),
  ...
)

Arguments

model

A multinomial-Poisson generalized linear mixed model created by glmer.mp.

formula

A formula object in the style of, e.g., pairwise ~ X1*X2, where X1 and X2 are factors in model. The pairwise keyword must be used on the left-hand side of the formula. See the specs entry for emmeans.

adjust

A string indicating the p-value adjustment to use. Defaults to "holm". See the Details section for p.adjust.

...

Additional arguments to be passed to glmer. Generally, these are unnecessary but are provided for advanced users. They should not specify formula, data, or family arguments. See glmer for valid arguments.

Details

Post hoc pairwise comparisons should be conducted only after a statistically significant omnibus test using Anova.mp. Comparisons are conducted in the style of emmeans but not using this function; rather, the multinomial-Poisson trick is used on a subset of the data relevant to each pairwise comparison.

Users wishing to verify the correctness of glmer.mp.con should compare its results to emmeans results for models built with glmer using family=binomial for dichotomous responses. The results should be similar.

Value

Pairwise comparisons for all levels indicated by the factors in formula.

Note

It is common to receive boundary (singular) fit messages. These generally can be ignored provided the test outputs look sensible. Less commonly, the procedures can fail to converge, which can happen when counts of one or more categories are very small or zero in some conditions. In such cases, any results should be regarded with caution.

Author(s)

Jacob O. Wobbrock

References

Baker, S.G. (1994). The multinomial-Poisson transformation. The Statistician 43 (4), pp. 495-504. doi:10.2307/2348134

Chen, Z. and Kuo, L. (2001). A note on the estimation of the multinomial logit model with random effects. The American Statistician 55 (2), pp. 89-95. https://www.jstor.org/stable/2685993

Guimaraes, P. (2004). Understanding the multinomial-Poisson transformation. The Stata Journal 4 (3), pp. 265-273. https://www.stata-journal.com/article.html?article=st0069

Lee, J.Y.L., Green, P.J.,and Ryan, L.M. (2017). On the “Poisson trick” and its extensions for fitting multinomial regression models. arXiv preprint available at doi:10.48550/arXiv.1707.08538

See Also

Anova.mp(), glmer.mp(), glm.mp(), glm.mp.con(), emmeans::emmeans()

Examples

library(car)
library(lme4)
library(lmerTest)
library(emmeans)

## within-subjects factors (x1,X2) with dichotomous response (Y)
data(ws2, package="multpois")

ws2$PId = factor(ws2$PId)
ws2$Y = factor(ws2$Y)
ws2$X1 = factor(ws2$X1)
ws2$X2 = factor(ws2$X2)
contrasts(ws2$X1) <- "contr.sum"
contrasts(ws2$X2) <- "contr.sum"

m1 = glmer(Y ~ X1*X2 + (1|PId), data=ws2, family=binomial)
Anova(m1, type=3)
emmeans(m1, pairwise ~ X1*X2, adjust="holm")

m2 = glmer.mp(Y ~ X1*X2 + (1|PId), data=ws2)
Anova.mp(m2, type=3)
glmer.mp.con(m2, pairwise ~ X1*X2, adjust="holm") # compare

## within-subjects factors (x1,X2) with polytomous response (Y)
data(ws3, package="multpois")

ws3$PId = factor(ws3$PId)
ws3$Y = factor(ws3$Y)
ws3$X1 = factor(ws3$X1)
ws3$X2 = factor(ws3$X2)
contrasts(ws3$X1) <- "contr.sum"
contrasts(ws3$X2) <- "contr.sum"

m3 = glmer.mp(Y ~ X1*X2 + (1|PId), data=ws3)
Anova.mp(m3, type=3)
glmer.mp.con(m3, pairwise ~ X1*X2, adjust="holm")


[Package multpois version 0.2.0 Index]