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

[Henrik Singmann]

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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