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

4 messages · Guillaume Adeux, Henrik Singmann

Original
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
5 days later
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:
1 day later
Guillaume Adeux
# 
Hi Henrik,

Thank you very much for this work. This is highly appreciated. Here is some
feedback...

I did this on my data with the following code:

#set_sum_contrasts()
#test_terms(Ratio~block+year_s+syst+syst:year_s,extra_
formula=~(1|year:plot)+(1|plot)+(1|year),est_fun=glmmTMB::glmmTMB,data=bio)
Here is the output (year_s is scale(year)):

glmmTMB::glmmTMB Anova Table (Type III tests)

Model: Ratio2 ~ block + year_s + syst * year_s + (1 | year:plot) + (1 |
Model:     plot) + (1 | year)
Data: bio
       Effect Df 1 Df 0   Chisq Pr(>Chisq)
1       block   15    1      NA       <NA>
2      year_s   15    1    0.00        .99
3        syst   15    4 10.75 *        .03
4 year_s:syst   15    4    0.01       >.99
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?+? 0.1 ? ? 1


-The function does not seem to recognize scale() when it is directly
applied in the formula (i.e. Ratio2~block+scale(year)+syst+syst:scale(year)).
The variable needs to be scaled outside of the formula. Otherwise, we get
an error message "Error in model.matrix.default(formula,data=new_data):
model frame and formula mismatch in model.matrix()". The same thing happens
if you want to add a small constant to the response (i.e. Ratio + 0.00001).
This needs to be done outside of the function.

-When a submodel has a convergence problem, the function is not capable of
doing the likelihood ratio test (cf. submodel in which "block" was
removed). This is not so surprising but it seems like like afex::mixed was
capable of doing so. I tried bumping up the number of iterations in arg_est
with arg_est=list(control=glmmTMBControl(...)) - which works - but did not
resolve the problem.

I can send you the data if you want to have a look see :) I'm afraid I
can't add much more.

Thanks again! You have a pending citation!

Guillaume ADEUX


2018-08-07 17:42 GMT+02:00 Henrik Singmann <singmann at psychologie.uzh.ch>:

  
  


  


      

    

    

      
      

        


  

        

      Henrik Singmann
    

    

      
      #
    

  


  

      
Hi Guillaume,

Thanks for your feedback. Indeed, the initial version could not handle 
fancy formulas. I have just updated the package on github to allow this 
and also ensured it works with glmmTMB:

library("monet")
library("glmmTMB")
## adapted from the vignette:
set_sum_contrasts()
Owls <- transform(Owls,
 ????????????????? Nest=reorder(Nest,NegPerChick),
 ????????????????? NCalls=SiblingNegotiation,
 ????????????????? FT=FoodTreatment)
zipp_test <- test_terms(formula = NCalls~(FT+scale(ArrivalTime))*SexParent +
 ????????????????????????? offset(log(BroodSize)),
 ??????????????????????? data = Owls, extra_formula = ~ (1|Nest),
 ??????????????????????? est_fun = glmmTMB,
 ??????????????????????? arg_est = list(ziformula=~1, family=poisson)
)
zipp_test
# glmmTMB Anova Table (Type III tests)
#
# Model: NCalls ~ (FT + scale(ArrivalTime)) * SexParent + 
offset(log(BroodSize)) +
# Model:???? (1 | Nest)
# Data: Owls
#???????????????????????? Effect Df 1 Df 0???? Chisq Pr(>Chisq)
# 1?????????????????????????? FT??? 8??? 1 20.03 ***???? <.0001
# 2?????????? scale(ArrivalTime)??? 8??? 1 58.62 ***???? <.0001
# 3??????????????????? SexParent??? 8??? 1????? 0.00?????? >.99
# 4???????????????? FT:SexParent??? 8??? 1????? 0.00?????? >.99
# 5 scale(ArrivalTime):SexParent??? 8??? 1????? 0.00?????? >.99
# ---
# Signif. codes:? 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?+? 0.1 ? ? 1

If you have some example data, where the LRT fails, I could take a look 
at it as well. Ideally, I would then add this as a test to the package. 
So preferably with some data that is available in some package or so. 
However, if this is not possible, I could also only run the tests only 
locally and keep your data private. In this case, send me the data offlist.

Cheers,
Henrik



Am 09.08.2018 um 10:20 schrieb Guillaume Adeux: