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., factor
s
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 |
formula |
A formula object in the style of, e.g., |
adjust |
A string indicating the p-value adjustment to use. Defaults to |
... |
Additional arguments to be passed to |
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")