Re: [R] a warning message from lmer

From: Lan Wei <lanwei_at_umich.edu>
Date: Sat, 12 Jul 2008 18:50:14 -0400

Thanks for reminding me. But, the problem still exists when I combine these two 'joy' levels.

Quoting Douglas Bates <bates_at_stat.wisc.edu>:

> By the way, did you notice that the levels of Emotion include both
> "joy" and "joy ". You may want to correct that.
>
> On Sat, Jul 12, 2008 at 7:47 AM, Douglas Bates <bates_at_stat.wisc.edu> wrote:
>> On Sat, Jul 12, 2008 at 6:23 AM, Lan Wei <lanwei_at_umich.edu> wrote:
>>> Hi all,
>>>
>>> I have a problem when running lmer.
>>> In my data set, Agree is a binary(0/1) response. WalkerID and ObsID is
>>> the identification number of the subjects. the description of the
>>> other variables are as follows:
>>>>
>>>> levels(regdat$Display)
>>>
>>> [1] "Dynamic" "Static"
>>>>
>>>> levels(regdat$Survey)
>>>
>>> [1] "HM1_A" "HM1_B" "HM1_C" "HM2_A" "HM2_B" "HM2_C" "ST_A" "ST_B"
>>> "ST_C"
>>>>
>>>> levels(regdat$Emotion)
>>>
>>> [1] "aneu" "ang" "con" "joy" "joy " "sad"
>>>>
>>>> levels(regdat$ObsGender)
>>>
>>> [1] "F" "M"
>>>>
>>>> levels(regdat$WalkerGender)
>>>
>>> [1] "F" "M"
>>>
>>> the watning is:
>>>>
>>> fit1<-lmer(Agree~Display+Survey+Emotion+WalkerGender+ObsGender+(1|WalkerID)+(1|ObsID),family=binomial(link='logit'),data=regdat)
>>> Warning message:
>>> In mer_finalize(ans, verbose) : gr cannot be computed at initial par
>>> (65)
>>
>>> Does anybody have some hint to solve this problem? I'd very much appreciate
>>> it!
>>
>> In situations like this it is best to add the argument
>>
>> verbose = TRUE
>>
>> in the call to lmer so that you can see the progress of the
>> iterations. (Also, you may want to call glmer directly. When you
>> call lmer with a non-gaussian family it simply calls glmer. You can
>> avoid the extra step.)
>>
>> This call is returning a warning about evaluation of the gradient at
>> the initial values of the parameters. I'm not sure if it then goes on
>> to optimize the approximated deviance.
>>
>> If the approximated deviance is not being minimized for this model you
>> may want to start with a simpler model, omitting some of the terms in
>> the fixed effects.
>>
>
>
>

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