[R-sig-ME] glmer conditional deviance DECREASING after removal of fixed effect??
Douglas Bates
dmb@te@ @end|ng |rom gm@||@com
Thu Aug 31 14:56:00 CEST 2023
You say you are comparing conditional deviances rather than marginal
deviances. Can you expand on that a bit further? How are you evaluating
the conditional deviances?
The models are being fit according to what you describe as the marginal
deviance - what I would call the deviance. It is not surprising that what
you are calling the conditional deviance is inconsistent with the nesting
of the models because they weren't fit according to that criterion.
julia> m01 = let f = @formula y ~ 1 + x1 + x2 + x3 + x4 + (1|id)
fit(MixedModel, f, dat, Bernoulli(); contrasts, nAGQ=9)
end
Minimizing 141 Time: 0:00:00 ( 1.22 ms/it)
Generalized Linear Mixed Model fit by maximum likelihood (nAGQ = 9)
y ~ 1 + x1 + x2 + x3 + x4 + (1 | id)
Distribution: Bernoulli{Float64}
Link: LogitLink()
logLik deviance AIC AICc BIC
-1450.8163 2900.8511 2915.6326 2915.6833 2955.5600
Variance components:
Column Variance Std.Dev.
id (Intercept) 0.270823 0.520406
Number of obs: 2217; levels of grouping factors: 331
Fixed-effects parameters:
────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
────────────────────────────────────────────────────
(Intercept) 0.0969256 0.140211 0.69 0.4894
x1: 1 -0.0449824 0.0553361 -0.81 0.4163
x2 0.0744891 0.0133743 5.57 <1e-07
x3 -0.548392 0.0914109 -6.00 <1e-08
x4: B 0.390359 0.0803063 4.86 <1e-05
x4: C 0.299932 0.0991249 3.03 0.0025
────────────────────────────────────────────────────
julia> m02 = let f = @formula y ~ 1 + x1 + x2 + x3 + (1|id)
fit(MixedModel, f, dat, Bernoulli(); contrasts, nAGQ=9)
end
Generalized Linear Mixed Model fit by maximum likelihood (nAGQ = 9)
y ~ 1 + x1 + x2 + x3 + (1 | id)
Distribution: Bernoulli{Float64}
Link: LogitLink()
logLik deviance AIC AICc BIC
-1472.4551 2944.0654 2954.9102 2954.9373 2983.4297
Variance components:
Column Variance Std.Dev.
id (Intercept) 0.274331 0.523766
Number of obs: 2217; levels of grouping factors: 331
Fixed-effects parameters:
────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
────────────────────────────────────────────────────
(Intercept) -0.448496 0.100915 -4.44 <1e-05
x1: 1 -0.0527684 0.0547746 -0.96 0.3354
x2 0.0694393 0.0131541 5.28 <1e-06
x3 -0.556903 0.0904798 -6.15 <1e-09
────────────────────────────────────────────────────
julia> MixedModels.likelihoodratiotest(m02, m01)
Model Formulae
1: y ~ 1 + x1 + x2 + x3 + (1 | id)
2: y ~ 1 + x1 + x2 + x3 + x4 + (1 | id)
──────────────────────────────────────────────────
model-dof deviance χ² χ²-dof P(>χ²)
──────────────────────────────────────────────────
[1] 5 2944.0654
[2] 7 2900.8511 43.2144 2 <1e-09
──────────────────────────────────────────────────
I would note that your data are so imbalanced with respect to id that it is
not surprising that you get unstable results. (I changed your id column
from integers to a factor so that 33 becomes S033.)
331×2 DataFrame
Row │ id nrow
│ String Int64
─────┼───────────────
1 │ S033 1227
2 │ S134 46
3 │ S295 45
4 │ S127 41
5 │ S125 33
6 │ S228 31
7 │ S193 23
8 │ S064 18
9 │ S281 16
10 │ S055 13
11 │ S035 13
12 │ S091 12
13 │ S175 11
14 │ S284 10
15 │ S159 10
⋮ │ ⋮ ⋮
317 │ S324 1
318 │ S115 1
319 │ S192 1
320 │ S201 1
321 │ S156 1
322 │ S202 1
323 │ S067 1
324 │ S264 1
325 │ S023 1
326 │ S090 1
327 │ S195 1
328 │ S170 1
329 │ S241 1
330 │ S189 1
331 │ S213 1
301 rows omitted
On Thu, Aug 31, 2023 at 3:02 AM Juho Kristian Ruohonen <
juho.kristian.ruohonen using gmail.com> wrote:
> Hi,
>
> I thought it was impossible for deviance to decrease when a term is
> removed!?!? Yet I'm seeing it happen with this pair of relatively simple
> Bernoulli GLMMs fit using lme4:glmer():
>
> > full <- glmer(y ~ (1|id) + x1 + x2 + x3 + x4, family = binomial, nAGQ =
> > 6, data = anon)
>
> > reduced <- update(full, ~. -x1)
>
> > c(full = deviance(full), reduced = deviance(reduced))
>
>
> * full reduced*
> *2808.671 2807.374 *
>
> What on earth going on? FYI, I am deliberately comparing conditional
> deviances rather than marginal ones, because quite a few of the clusters
> are of inherent interest and likely to recur in future data.
>
> My anonymized datafile is attached.
>
> Best,
>
> Juho
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
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