Anova.glmmTMB | R Documentation |
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.
Support for emmeans also allows additional options
component = "response"
(response means taking both the cond
and
zi
components into account), and component = "cmean"
(mean of the
[possibly truncated] conditional distribution).
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, or predictions with type = "response"
or
type = "component"
. Note: In hurdle models,
component = "cmean"
produces means
of the truncated conditional distribution, while
component = "cond", type = "response"
produces means of the untruncated
conditional distribution.
the effects
package computes graphical tabular effect displays
(only for the fixed effects of the conditional component)
Anova.glmmTMB(
mod,
type = c("II", "III", 2, 3),
test.statistic = c("Chisq", "F"),
component = "cond",
vcov. = vcov(mod)[[component]],
singular.ok,
include.rankdef.cols = FALSE,
...
)
Effect.glmmTMB(focal.predictors, mod, ...)
mod |
a glmmTMB model |
type |
type of test, |
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") or, in emmeans only, "response" or "cmean" as described in Details. |
vcov. |
variance-covariance matrix (usually extracted automatically) |
singular.ok |
OK to do ANOVA with singular models (unused) ? |
include.rankdef.cols |
include all columns of a rank-deficient model matrix? |
... |
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. |
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, ...)
.
warp.lm <- glmmTMB(breaks ~ wool * tension, data = warpbreaks)
salamander1 <- up2date(readRDS(system.file("example_files","salamander1.rds",package="glmmTMB")))
if (require(emmeans)) withAutoprint({
emmeans(warp.lm, poly ~ tension | wool)
emmeans(salamander1, ~ mined, type="response") # conditional means
emmeans(salamander1, ~ mined, component="cmean") # same as above, but re-gridded
emmeans(salamander1, ~ mined, component="zi", type="response") # zero probabilities
emmeans(salamander1, ~ mined, component="response") # response means including both components
})
if (getRversion() >= "3.6.0") {
if (require(car)) withAutoprint({
Anova(warp.lm,type="III")
Anova(salamander1)
Anova(salamander1, component="zi")
})
if (require(effects)) withAutoprint({
plot(allEffects(warp.lm))
plot(allEffects(salamander1))
})
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.