Avoid errors in pwrssUpdate ?

I am getting a lot of pwrssUpdate errors when trying to model my
data with gmler (using the most recent version from svn... I don't
know if the previous versions were affected).

These errors are "PIRLS step failed" or "pwrssUpdate did not
converge in 30 iterations". I understand that these means that the
algorithm does not manage to work with my data, but there is a
peculiar behavior, and my data doesn't seem too unreasonable to fit
with the model I want to use, so I am wondering if the problem is on
my side !

Here is the model I want to fit, which doesn't seem unreasonable
given the figure (random slopes and intercepts for subjects) :

 model<-glmer(cbind(RuleReach,NTrials-RuleReach)~RuleWeight+
 (RuleWeight|Subject),data=rewardalldirsub,family=binomial)

However this gives me the "pwrssUpdate did not converge in 30
iterations" error.  What is surprinsing, is that if I do not use the
rightmost points (RuleWeight of 1), the model converges, even though
there are less datapoints and the remaining points are the noisiest
(subjects follow the rule quite reliably when it has a weight of 1
as you can see).

Removing the correlation in the random effects works sometimes (but
not on all my sub data sets), and having a random intercept only
(which is obviously not correct) is the only thing that seems to
work in all cases.

Centering RuleWeight (ie having it between -1 and 1 instead of 0 and
1) doesn't work.  Any ideas on why this doesn't work / how to make
it work ?

Pierre Morel <pier.morel at ...> writes:

I am getting a lot of pwrssUpdate errors when trying to model my
data with gmler (using the most recent version from svn... I don't
know if the previous versions were affected).

These errors are "PIRLS step failed" or "pwrssUpdate did not
converge in 30 iterations". I understand that these means that the
algorithm does not manage to work with my data, but there is a
peculiar behavior, and my data doesn't seem too unreasonable to fit
with the model I want to use, so I am wondering if the problem is on
my side !

[snip to make gmane happier]

Here is the model I want to fit, which doesn't seem unreasonable
given the figure (random slopes and intercepts for subjects) :

model<-glmer(cbind(RuleReach,NTrials-RuleReach)~RuleWeight+
(RuleWeight|Subject),data=rewardalldirsub,family=binomial)

However this gives me the "pwrssUpdate did not converge in 30
iterations" error.  What is surprinsing, is that if I do not use the
rightmost points (RuleWeight of 1), the model converges, even though
there are less datapoints and the remaining points are the noisiest
(subjects follow the rule quite reliably when it has a weight of 1
as you can see).

Removing the correlation in the random effects works sometimes (but
not on all my sub data sets), and having a random intercept only
(which is obviously not correct) is the only thing that seems to
work in all cases.

Centering RuleWeight (ie having it between -1 and 1 instead of 0 and
1) doesn't work.  Any ideas on why this doesn't work / how to make
it work ?

 Thanks for the report: this is an issue the developers are
(painfully) aware of, and working on.  The issue arises mostly when
the predictions for some observations are very close to 0 or 1 (which
explains why using the rightmost points helps ...)  You have tried all
the obvious things I know of.  I would additionally try (1) setting
starting values by hand and/or (2) trying out glmmADMB ...

 Ben Bolker

OK, thanks for the suggestion of glmmADMB, I will look that up.
I also found out that I could get things working with nAGQ=0.
However the doc says it's less exact, and profile() gives 

error in this case.
Pierre Morel
Le 13 juin 2012 ? 10:54, Ben Bolker a ?crit :

Pierre Morel <pier.morel at ...> writes:

I am getting a lot of pwrssUpdate errors when trying to model my
data with gmler (using the most recent version from svn... I don't
know if the previous versions were affected).
These errors are "PIRLS step failed" or "pwrssUpdate did not
converge in 30 iterations". I understand that these means that the
algorithm does not manage to work with my data, but there is a
peculiar behavior, and my data doesn't seem too unreasonable to fit
with the model I want to use, so I am wondering if the problem is on
my side !

[snip to make gmane happier]

Here is the model I want to fit, which doesn't seem unreasonable
given the figure (random slopes and intercepts for subjects) :

model<-glmer(cbind(RuleReach,NTrials-RuleReach)~RuleWeight+
(RuleWeight|Subject),data=rewardalldirsub,family=binomial)

However this gives me the "pwrssUpdate did not converge in 30
iterations" error.  What is surprinsing, is that if I do not use the
rightmost points (RuleWeight of 1), the model converges, even though
there are less datapoints and the remaining points are the noisiest
(subjects follow the rule quite reliably when it has a weight of 1
as you can see).

Removing the correlation in the random effects works sometimes (but
not on all my sub data sets), and having a random intercept only
(which is obviously not correct) is the only thing that seems to
work in all cases.

Centering RuleWeight (ie having it between -1 and 1 instead of 0 and
1) doesn't work.  Any ideas on why this doesn't work / how to make
it work ?

 Thanks for the report: this is an issue the developers are
(painfully) aware of, and working on.  The issue arises mostly when
the predictions for some observations are very close to 0 or 1 (which
explains why using the rightmost points helps ...)  You have tried all
the obvious things I know of.  I would additionally try (1) setting
starting values by hand and/or (2) trying out glmmADMB ...

 Ben Bolker