Incorrect output from nested model with mapped pars
?? Your first question looks like it could possibly be a bug, so please post it on the glmmTMB github issues list (ideally with a reproducible example!) ?? For your second question: using a t distribution implies knowing the appropriate residual df, which is very difficult (see the GLMM FAQ or any of the many documents floating around on the web for why the sampling distributions of parameters from complex models are only approximately t-distributed anyway, and why it is so hard to find good approximations ...)
On 6/30/20 9:22 PM, Christopher Nottingham wrote:
One more question. Why is the summary function giving the z value and associated p-value. A gaussian error structure is assumed, so shouldn't t values be used to obtain p values. Thanks, Chris From: Christopher Nottingham Sent: Tuesday, 30 June 2020 5:06 PM To: 'r-sig-mixed-models at r-project.org' <r-sig-mixed-models at r-project.org> Subject: Incorrect output from nested model with mapped pars I have a dataset with a variable labelled n_comm that is not relevant to some factor level combinations. I am fitting a nested model to this data and fixing betas representing the irrelevant factor combinations to 0 using map. As shown following, the model output from the summary table does not match what should be produced.
map_names = list(beta = factor(c(1:6, NA, 8))) fit = glmmTMB(log(Err) ~ model + n_surv + species + n_comm:geostatistical + intensity,
+ data = Bhat_all.df, + start = list(beta = ifelse(is.na(map_names$beta), 0, 1)), + map = map_names)
summary(fit)
Family: gaussian ( identity )
Formula: log(Err) ~ model + n_surv + species + n_comm:geostatistical + intensity
Data: Bhat_all.df
AIC BIC logLik deviance df.resid
18340.8 18394.7 -9162.4 18324.8 6212
Dispersion estimate for gaussian family (sigma^2): 1.11
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 9.329e+00 5.461e-02 170.82 <2e-16 ***
modelbiomass-dynamics -4.082e+00 5.089e-02 -80.22 <2e-16 ***
modelsize-structured -4.092e+00 5.056e-02 -80.93 <2e-16 ***
n_surv -8.895e-04 8.146e-05 -10.92 <2e-16 ***
species$\\mathit{S. aequilatera}$ 5.596e-01 2.677e-02 20.90 <2e-16 ***
intensityFishing intensity: high 3.589e-01 2.681e-02 13.39 <2e-16 ***
n_comm:geostatisticalFALSE 0.000e+00 7.450e-05 0.00 1.000
n_comm:geostatisticalTRUE -3.245e-04 5.461e-02 -0.01 0.995
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning message:
In cbind(Estimate = coefs, `Std. Error` = sqrt(diag(vcov))) :
number of rows of result is not a multiple of vector length (arg 2)
rbind(sqrt(diag(solve(fit$obj$he()))))
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 0.05458617 0.05086506 0.05053495 8.142355e-05 0.02675588 0.02679859 7.446289e-05 0.01792267
fit$sdr
sdreport(.) result
Estimate Std. Error
beta 9.3291879616 5.461347e-02
beta -4.0822725635 5.089050e-02
beta -4.0920631052 5.056022e-02
beta -0.0008895195 8.146427e-05
beta 0.5595933469 2.676926e-02
beta 0.3589326271 2.681199e-02
beta -0.0003244617 7.450013e-05
betad 0.1082286532 1.793163e-02
Maximum gradient component: 0.001913387
The output below is wrong (there should be no std err, on a mapped value and the other values are incorrect.),
n_comm:geostatisticalTRUE -3.245e-04 5.461e-02 -0.01 0.995
The dataset is attached as a rds for reproducibility.
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