[R-sig-ME] Fwd: Fixing singularity in a generalized linear mixed effect model
b|e|||@@|e@@@ndr@ @end|ng |rom gm@||@com
Thu Mar 21 21:43:07 CET 2019
Sorry if I am reposting this, I am not sure it got sent to the list...
---------- Forwarded message ---------
From: Alessandra Bielli <bielli.alessandra using gmail.com>
Date: Wed, Mar 20, 2019 at 10:18 PM
Subject: Fixing singularity in a generalized linear mixed effect model
To: <r-sig-mixed-models using r-project.org>
I am fitting this model using the lme4 package, in order to obtain catch
estimates using the predict function
m1 <- glmer(Count ~ CE + offset(log(Effort)) + (1|SetYear) +(1|Season) +
(1|Lance.N) + (1|Boat.Name) + (1|Observer.Name), data =
Data, glmerControl(optimizer = "bobyqa"), family=
where: CE is a categorical (control or treatment), Effort is numerical
(fishing effort), and all the other variables are random effects.
*My problem is that I get a warning message saying that the model is
Generalized linear mixed model fit by maximum likelihood (Laplace
Family: poisson ( log )
Formula: Count ~ CE + offset(log(Effort)) + (1 | SetYear) + (1 |
Season) + (1 | Lance.N) + (1 | Boat.Name) + (1 | Observer.Name)
Control: glmerControl(optimizer = "bobyqa")
AIC BIC logLik deviance df.resid
148.6 174.3 -67.3 134.6 285
Min 1Q Median 3Q Max
-0.4852 -0.1758 -0.1339 -0.1227 3.5980
Groups Name Variance Std.Dev.
Lance.N (Intercept) 2.259e+00 1.503e+00
Boat.Name (Intercept) 0.000e+00 0.000e+00
Observer.Name (Intercept) 0.000e+00 0.000e+00
Season (Intercept) 4.149e-17 6.442e-09
SetYear (Intercept) 0.000e+00 0.000e+00
Number of obs: 292, groups:
Lance.N, 146; Boat.Name, 21; Observer.Name, 5; Season, 4; SetYear, 4
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.5751 0.6612 -3.895 9.83e-05 ***
CEE -0.5878 0.5003 -1.175 0.24
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
*convergence code: 0*
I am aware that there are a lot of random effects and some of them have a
number of levels <5. However, this study was carried out under real fishery
conditions, so these random effects seemed all important to me.
I removed the random effects with variance zero as suggested here
until I removed them all and found myself with a glm instead.
My questions are
- why the variance of Lance.N, initially positive, becomes zero after I
remove the other random effects that had variance equal zero?
- is it acceptable to fit a glm just because all the random effect
variances were zero?
I hope I gave all the information you need.
Thanks for any advice!
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