Error message when handling complete separation

In GLMMadaptive you can solve separation issues by including a penalty
term for the coefficients. You may find an example on how to do this at the
bottom of this vignette:
https://drizopoulos.github.io/GLMMadaptive/articles/GLMMadaptive_basics.html


Best,
Dimitris

*From: *C. AMAL D. GLELE <altessedac2 at gmail.com>
*Date: *Monday, 21 Oct 2019, 13:21
*To: *R SIG Mixed Models <r-sig-mixed-models at r-project.org>
*Subject: *[R-sig-ME] Error message when handling complete separation

Hi, all.
When trying to handle a complete separation case
( initialmodel<-glmer(resp~treatment+(1|net),family=binomial,data=mydata) ,
where: treatment is a factor with 4 levels; net has 4 levels;
resp<-cbind(,)
Warning message:
unable to evaluate scaled gradient Hessian is numerically singular:
parameters are not uniquely determined
Outputs: huge estimates, huges sderror and p-value=1 everywhere
)
to solve this, I tried the following fit;
mod2kisBW_unsep1<-

bglmer(respkisBW~treatment+(1|net),data=conedata1kisBW,family=binomial,fixef.prior
= normal(cov=diag(9,4)))
but, I get the error message below:

Error in length(value <- as.numeric(value)) == 1L :
  pwrssUpdate did not converge in (maxit) iterations

1) what are the possible causes of such a problem?
2) how can I figure it out?
Thanks, in advance.
Regards,

        [[alternative HTML version deleted]]

Ok;
thank you so much for reply and your suggestion.
Regards,

Le lun. 21 oct. 2019 ? 13:32, D. Rizopoulos <d.rizopoulos at erasmusmc.nl> a
?crit :

In GLMMadaptive you can solve separation issues by including a penalty
term for the coefficients. You may find an example on how to do this at the
bottom of this vignette:
https://drizopoulos.github.io/GLMMadaptive/articles/GLMMadaptive_basics.html


Best,
Dimitris

*From: *C. AMAL D. GLELE <altessedac2 at gmail.com>
*Date: *Monday, 21 Oct 2019, 13:21
*To: *R SIG Mixed Models <r-sig-mixed-models at r-project.org>
*Subject: *[R-sig-ME] Error message when handling complete separation

Hi, all.
When trying to handle a complete separation case
( initialmodel<-glmer(resp~treatment+(1|net),family=binomial,data=mydata) ,
where: treatment is a factor with 4 levels; net has 4 levels;
resp<-cbind(,)
Warning message:
unable to evaluate scaled gradient Hessian is numerically singular:
parameters are not uniquely determined
Outputs: huge estimates, huges sderror and p-value=1 everywhere
)
to solve this, I tried the following fit;
mod2kisBW_unsep1<-

bglmer(respkisBW~treatment+(1|net),data=conedata1kisBW,family=binomial,fixef.prior
= normal(cov=diag(9,4)))
but, I get the error message below:

Error in length(value <- as.numeric(value)) == 1L :
  pwrssUpdate did not converge in (maxit) iterations

1) what are the possible causes of such a problem?
2) how can I figure it out?
Thanks, in advance.
Regards,

        [[alternative HTML version deleted]]