Hello I am currently working the shrinkage phenomenon in multilevel models. I have a problem of convergence in the model when I add more random coefficients to the models, after three coefficients,I have this error message: Warning message: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0278398 (tol = 0.001, component 1) I have a three-level model, with Poisson distribution, the structure is: to my subject (rat) I register the vocalizations emitted in four specific moments of an experiment that is repeated for three days. And I have 31 subjects. All my variables are dichotomous. I tried several alternatives to solve that error, but the only effective thing was to specify the optimizer = ?bobyqa? and more iterations, for my luck it worked, F.aleat.int <- glmer(y ~ 1+DIA2+DIA3+M1+M4+M5+M6+M9+M10+M11+ (1 | ID_DIA:ID_SUJ) + (1+M1+M5+M11 | ID_SUJ), family=poisson, data=base, control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e4))) But the estimates of random effects are much greater than the obtained using other packages (MCMCglmm, glmmLasso, glmmsr and hglm), For example, for one variable I have 2.1 and in the other packages I have 0.8 How can I explain that: - Due to the nature of the model? Or the optimizer as such? - Would you appreciate it if you could tell me how I can solve that? - Is it because all my variables are dichotomous? Regards
Help with multilevel model Poisson
4 messages · Thierry Onkelinx, Andrea Céspedes
3 days later
Dear Andrea, How many observation per subject? 12? That is too few to fit a random slope model with 10 parameters. ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be Havenlaan 88 bus 73, 1000 Brussel www.inbo.be /////////////////////////////////////////////////////////////////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey /////////////////////////////////////////////////////////////////////////////////////////// <https://www.inbo.be> Op wo 9 okt. 2019 om 16:38 schreef Andrea C?spedes <ancelis.07 at gmail.com>:
Hello
I am currently working the shrinkage phenomenon in multilevel models.
I have a problem of convergence in the model when I add more random
coefficients to the models, after three coefficients,I have this error
message:
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0278398 (tol = 0.001,
component 1)
I have a three-level model, with Poisson distribution, the structure is: to
my subject (rat) I register the vocalizations emitted in four specific
moments of an experiment that is repeated for three days. And I have 31
subjects. All my variables are dichotomous.
I tried several alternatives to solve that error, but the only effective
thing was to specify the optimizer = ?bobyqa? and more iterations, for my
luck it worked,
F.aleat.int <- glmer(y ~ 1+DIA2+DIA3+M1+M4+M5+M6+M9+M10+M11+ (1 |
ID_DIA:ID_SUJ) + (1+M1+M5+M11 | ID_SUJ), family=poisson, data=base,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e4)))
But the estimates of random effects are much greater than the obtained
using other packages (MCMCglmm, glmmLasso, glmmsr and hglm),
For example, for one variable I have 2.1 and in the other packages I have
0.8
How can I explain that:
- Due to the nature of the model? Or the optimizer as such?
- Would you appreciate it if you could tell me how I can solve that?
- Is it because all my variables are dichotomous?
Regards
[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
Hi everyone No, it?s 12 observations per day and it?s three days, so I have 36 observations per subject Thanks El mi?., 9 oct. 2019 a las 9:04, Thierry Onkelinx (<thierry.onkelinx at inbo.be>) escribi?:
Dear Andrea, How many observation per subject? 12? That is too few to fit a random slope model with 10 parameters. ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be Havenlaan 88 bus 73, 1000 Brussel www.inbo.be /////////////////////////////////////////////////////////////////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey /////////////////////////////////////////////////////////////////////////////////////////// <https://www.inbo.be> Op wo 9 okt. 2019 om 16:38 schreef Andrea C?spedes <ancelis.07 at gmail.com>:
Hello
I am currently working the shrinkage phenomenon in multilevel models.
I have a problem of convergence in the model when I add more random
coefficients to the models, after three coefficients,I have this error
message:
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0278398 (tol = 0.001,
component 1)
I have a three-level model, with Poisson distribution, the structure is:
to
my subject (rat) I register the vocalizations emitted in four specific
moments of an experiment that is repeated for three days. And I have 31
subjects. All my variables are dichotomous.
I tried several alternatives to solve that error, but the only effective
thing was to specify the optimizer = ?bobyqa? and more iterations, for my
luck it worked,
F.aleat.int <- glmer(y ~ 1+DIA2+DIA3+M1+M4+M5+M6+M9+M10+M11+ (1 |
ID_DIA:ID_SUJ) + (1+M1+M5+M11 | ID_SUJ), family=poisson, data=base,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e4)))
But the estimates of random effects are much greater than the obtained
using other packages (MCMCglmm, glmmLasso, glmmsr and hglm),
For example, for one variable I have 2.1 and in the other packages I have
0.8
How can I explain that:
- Due to the nature of the model? Or the optimizer as such?
- Would you appreciate it if you could tell me how I can solve
that?
- Is it because all my variables are dichotomous?
Regards
[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
36 observations per level is still very little to fit a 10 parameter random effect Op wo 9 okt. 2019 18:07 schreef Andrea C?spedes <ancelis.07 at gmail.com>:
Hi everyone No, it?s 12 observations per day and it?s three days, so I have 36 observations per subject Thanks El mi?., 9 oct. 2019 a las 9:04, Thierry Onkelinx (< thierry.onkelinx at inbo.be>) escribi?:
Dear Andrea, How many observation per subject? 12? That is too few to fit a random slope model with 10 parameters. ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be Havenlaan 88 bus 73, 1000 Brussel www.inbo.be /////////////////////////////////////////////////////////////////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey /////////////////////////////////////////////////////////////////////////////////////////// <https://www.inbo.be> Op wo 9 okt. 2019 om 16:38 schreef Andrea C?spedes <ancelis.07 at gmail.com
:
Hello
I am currently working the shrinkage phenomenon in multilevel models.
I have a problem of convergence in the model when I add more random
coefficients to the models, after three coefficients,I have this error
message:
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0278398 (tol = 0.001,
component 1)
I have a three-level model, with Poisson distribution, the structure is:
to
my subject (rat) I register the vocalizations emitted in four specific
moments of an experiment that is repeated for three days. And I have 31
subjects. All my variables are dichotomous.
I tried several alternatives to solve that error, but the only effective
thing was to specify the optimizer = ?bobyqa? and more iterations, for my
luck it worked,
F.aleat.int <- glmer(y ~ 1+DIA2+DIA3+M1+M4+M5+M6+M9+M10+M11+ (1 |
ID_DIA:ID_SUJ) + (1+M1+M5+M11 | ID_SUJ), family=poisson, data=base,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e4)))
But the estimates of random effects are much greater than the obtained
using other packages (MCMCglmm, glmmLasso, glmmsr and hglm),
For example, for one variable I have 2.1 and in the other packages I have
0.8
How can I explain that:
- Due to the nature of the model? Or the optimizer as such?
- Would you appreciate it if you could tell me how I can solve
that?
- Is it because all my variables are dichotomous?
Regards
[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models