glm.mp.con: Contrast tests for multinomial-Poisson GLM

In multpois: Analyze Nominal Response Data with the Multinomial-Poisson Trick

Contrast tests for multinomial-Poisson GLM

Description

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

Usage

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

Arguments

model	A multinomial-Poisson generalized linear model created by glm.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 for p.adjust.
...	Additional arguments to be passed to glm. Generally, these are unnecessary but are provided for advanced users. They must not pass formula, data, or family arguments. See glm 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 the subset of the data relevant to each pairwise comparison.

Users wishing to verify the correctness of glm.mp.con should compare its results to emmeans results for models built with glm using family=binomial for dichotomous responses. Factor contrasts should be set to sum-to-zero contrasts (i.e., "contr.sum"). The results should be similar.

Value

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

Author(s)

Jacob O. Wobbrock

References

Baker, S.G. (1994). The multinomial-Poisson transformation. The Statistician 43 (4), pp. 495-504. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/2348134")}

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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.1707.08538")}

See Also

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

Examples

library(multpois)
library(car)
library(nnet)
library(emmeans)

## two between-subjects factors (X1,X2) with dichotomous response (Y)
data(bs2, package="multpois")

bs2$PId = factor(bs2$PId)
bs2$Y = factor(bs2$Y)
bs2$X1 = factor(bs2$X1)
bs2$X2 = factor(bs2$X2)
contrasts(bs2$X1) <- "contr.sum"
contrasts(bs2$X2) <- "contr.sum"

m1 = glm(Y ~ X1*X2, data=bs2, family=binomial)
Anova(m1, type=3)
emmeans(m1, pairwise ~ X1*X2, adjust="holm")

m2 = glm.mp(Y ~ X1*X2, data=bs2)
Anova.mp(m2, type=3)
glm.mp.con(m2, pairwise ~ X1*X2, adjust="holm") # compare

## two between-subjects factors (X1,X2) with polytomous response (Y)
data(bs3, package="multpois")

bs3$PId = factor(bs3$PId)
bs3$Y = factor(bs3$Y)
bs3$X1 = factor(bs3$X1)
bs3$X2 = factor(bs3$X2)
contrasts(bs3$X1) <- "contr.sum"
contrasts(bs3$X2) <- "contr.sum"

m3 = multinom(Y ~ X1*X2, data=bs3, trace=FALSE)
Anova(m3, type=3)
emmeans::test(
  contrast(emmeans(m3, ~ X1*X2 | Y, mode="latent"), method="pairwise", ref=1),
  joint=TRUE, by="contrast"
)

m4 = glm.mp(Y ~ X1*X2, data=bs3)
Anova.mp(m4, type=3)
glm.mp.con(m4, pairwise ~ X1*X2, adjust="holm") # compare

multpois documentation built on April 3, 2025, 9:37 p.m.