glmmTMB: downstream_methods – R documentation

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downstream_methods

Downstream methods

Description

Methods have been written that allow glmmTMB objects to be used with several downstream packages that enable different forms of inference. For some methods (Anova and emmeans, but not effects at present), set the component argument to "cond" (conditional, the default), "zi" (zero-inflation) or "disp" (dispersion) in order to produce results for the corresponding part of a glmmTMB model.

In particular,

car::Anova constructs type-II and type-III Anova tables for the fixed effect parameters of any component
the emmeans package computes estimated marginal means (previously known as least-squares means) for the fixed effects of any component
the effects package computes graphical tabular effect displays (only for the fixed effects of the conditional component)

Usage

Anova.glmmTMB(
  mod,
  type = c("II", "III", 2, 3),
  test.statistic = c("Chisq", "F"),
  component = "cond",
  vcov. = vcov(mod)[[component]],
  singular.ok,
  ...
)

Effect.glmmTMB(focal.predictors, mod, ...)

Arguments

`mod`	a glmmTMB model
`type`	type of test, `"II"`, `"III"`, `2`, or `3`. Roman numerals are equivalent to the corresponding Arabic numerals. See `Anova` for details.
`test.statistic`	unused: only valid choice is "Chisq" (i.e., Wald chi-squared test)
`component`	which component of the model to test/analyze ("cond", "zi", or "disp")
`vcov.`	variance-covariance matrix (usually extracted automatically)
`singular.ok`	OK to do ANOVA with singular models (unused) ?
`...`	Additional parameters that may be supported by the method.
`focal.predictors`	a character vector of one or more predictors in the model in any order.

Details

While the examples below are disabled for earlier versions of R, they may still work; it may be necessary to refer to private versions of methods, e.g. glmmTMB:::Anova.glmmTMB(model, ...).

Examples

warp.lm <- glmmTMB(breaks ~ wool * tension, data = warpbreaks)
salamander1 <- readRDS(system.file("example_files","salamander1.rds",package="glmmTMB"))
if (require(emmeans)) {
    emmeans(warp.lm, poly ~ tension | wool)
    emmeans(salamander1, ~ mined, type="response")
    emmeans(salamander1, ~ mined, component="zi", type="response")
}
if (getRversion() >= "3.6.0") {
   if (require(car)) {
       Anova(warp.lm,type="III")
       Anova(salamander1)
       Anova(salamander1, component="zi")
   }
   if (require(effects)) {
       plot(allEffects(warp.lm))
       plot(allEffects(salamander1))
   }
}

glmmTMB

Generalized Linear Mixed Models using Template Model Builder

v1.0.2.1

AGPL-3

Authors

Arni Magnusson [aut] (<https://orcid.org/0000-0003-2769-6741>), Hans Skaug [aut], Anders Nielsen [aut] (<https://orcid.org/0000-0001-9683-9262>), Casper Berg [aut] (<https://orcid.org/0000-0002-3812-5269>), Kasper Kristensen [aut], Martin Maechler [aut] (<https://orcid.org/0000-0002-8685-9910>), Koen van Bentham [aut], Ben Bolker [aut] (<https://orcid.org/0000-0002-2127-0443>), Nafis Sadat [ctb] (<https://orcid.org/0000-0001-5715-616X>), Daniel Lüdecke [ctb] (<https://orcid.org/0000-0002-8895-3206>), Russ Lenth [ctb], Joseph O'Brien [ctb] (<https://orcid.org/0000-0001-9851-5077>), Mollie Brooks [aut, cre] (<https://orcid.org/0000-0001-6963-8326>)

Initial release