feedback: Anova (type III-tests) table based on LRT for glmmTMB models (drop1, anova, mixed)

Hi all,

It took me some time, but I managed to come up with something that might
be of help here. Specifically, I have worked on a new package, monet, that
runs Type III like tests with arbitrary estimation function using anova()
(i.e., LRT) as default. see: https://github.com/singmann/monet
It basically generalizes what afex::mixed does for arbitrary estimation
and test functions.

I do not have glmmTMB installed, but as of now it works with lm and
lme4::lmer. The main function is test_term(). It allows to pass two
formulas, one for which submodels are created and estimates (i.e., the
fixed-effects part) and one additional part which can for example hold the
random-effects part.

devtools::install_github("singmann/monet")
library("monet")
set_sum_contrasts() ## quite important, currently coding is not checked
data("Machines", package = "MEMSS")

# ignoring repeated-measures
m1 <- test_terms(score ~ Machine, data=Machines, est_fun = lm)
m1
# lm Anova Table (Type III tests)
#
# Model: score ~ Machine
# Data: Machines
#    Effect Df 1 Df 0         F Pr(>F)
# 1 Machine   51    2 26.30 *** <.0001
# ---
# Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?+? 0.1 ? ? 1

# simple model with random-slopes for repeated-measures factor
m3 <- test_terms(score ~ Machine, data=Machines,
                 extra_formula = ~ (Machine|Worker),
                 est_fun = lme4::lmer, arg_est = list(REML = FALSE),
                 arg_test = list(model.names=c("f", "r")))
m3
# lme4::lmer Anova Table (Type III tests)
#
# Model: score ~ Machine + (Machine | Worker)
# Data: Machines
#    Effect Df 1 Df 0     Chisq Pr(>Chisq)
# 1 Machine   10    2 17.14 ***      .0002
# ---
# Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?+? 0.1 ? ? 1

It returns an object of class "monet" with print(), nice(), and anova()
methods. The package is also quite lightweight and has no strong
dependencies besides to stats.

It it does not work with glmmTMB, a reproducible example would be great.
And I am of course happy to hear of any other comments.

Cheers,
Henrik



Am 02.08.2018 um 09:22 schrieb Guillaume Adeux:

Sorry, my intent was definetely not to shortcut anyone or anything.

I thought I would give you all a little feedback on your propositions.

The problem with drop1 (when an interaction is in the model) is when you
want it to behave like a type III anova. This results in one of the
variables having zero degree of freedom, hence LRT of 0 and no p-value.

Here is the example on an output:



Model: mod=glmmTMB(ratio ~ block + trt * time
+(1|plot)+(1|year)+(1|year:plot),family=list(family="beta",
link="logit"),data=biomass)

drop1(mod,.~block+trt*time,test=?Chisq?)

Single term deletions

                                Df                           AIC
LRT                      Pr(>Chi)

<none>                                                -5092.2

block                    1                             -5093.5
0.7345                  0.391415

trt                         4                             -5082.7
17.5018                0.001544

time                      0                             -5092.2
0

trt:time                  4                             -5100.0
0.2169                  0.994527


If I understand correctly, drop1 is incapable of comparing a model A+A:B
with A+B+A:B.

Anova.III.glmmTMB indeed works but only yields Wald tests which I thought
were not ideal for glmms.

I contacted the developper of the {afex} package to see if the mixed
function could be adapted to run on glmmTMB objects but no luck as of now.
Considering the framework of glmmTMB is similar to glmer, maybe this can
be
easily adapted.

Thanks for your interest. Don't hesitate if you have any input.

Guillaume ADEUX

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