[R-sig-ME] GLMM linearity checking

[Shujuan Feng]

The attach plot may not be visible.

The plot is like this : One big group residuals cluster around 1.2( from 1 
to 1.5), another big group residuals cluster around -0.8( from -0.6 to -1). 
There are no any values between -0.6 and 1.

 Thanks!







----- Original Message ----- 
From: "Shujuan Feng" <fengsj at mail.utexas.edu>
To: "R Mixed Models" <r-sig-mixed-models at r-project.org>
Sent: Thursday, June 03, 2010 2:40 PM
Subject: Re: [R-sig-ME] GLMM linearity checking


> Thanks Ben, it works.
>
> Then I feel there may be problems with the model by looking at the residul
> plots.
>
> When I just plot the residuals, I get two big group values (the plots of
> residual~predicotr are also like this). See the attach file: 
> residaul.jpeg.
>
> I have difficulty in imaging the residuals in GLM with the original
> dependent are 0s or 1s. Residuals should be the difference between the
> observed and the predicted.  I can understand the predicted in terms of
> transformed scale(logit), but I don't know how the observed 0s and 1s are
> transformed. Are the two group residual values from the 0s and 1s
> respectively?  Have anyone see such residuals?
>
> Thanks!
>
>
>
> PS: In my model, I have three continuous predictors, the dependent are 0s 
> or
> 1s and I use binomial(link = "logit"). I tried GLMM and also just GLM, I 
> got
> similar residual plots.
>
>
>
>
>
>
>
>
>
> ----- Original Message ----- 
> From: "Ben Bolker" <bolker at ufl.edu>
> To: "Shujuan Feng" <fengsj at mail.utexas.edu>
> Cc: "R Mixed Models" <r-sig-mixed-models at r-project.org>
> Sent: Thursday, June 03, 2010 1:37 PM
> Subject: Re: [R-sig-ME] GLMM linearity checking
>
>
>>  [cc'ing back to r-sig-mixed]
>>
>>  That was going to be my suggestion.
>>  Try omitting rows of the data set with NA predictors or responses
>> (na.omit() will work if your data frame does not have *other* columns
>> with NAs in them beyond those used in the model) before you start.
>>
>>
>> Shujuan Feng wrote:
>>> or maybe I can just delete the missing rows.
>>>
>>>
>>> ----- Original Message ----- 
>>> From: "Shujuan Feng" <fengsj at mail.utexas.edu>
>>> To: "Ben Bolker" <bolker at ufl.edu>
>>> Cc: <r-sig-mixed-models at r-project.org>
>>> Sent: Thursday, June 03, 2010 12:10 PM
>>> Subject: Re: [R-sig-ME] GLMM linearity checking
>>>
>>>
>>>> Thanks so much!
>>>>
>>>> I read about Graphical checking for GLMM (transformed by the link
>>>> Function) before fitting the model from a paper. I have difficulty in
>>>> imaging how the 0s and 1s are transformed by the ink. .....
>>>>
>>>> I tried your suggestions and this way should give me more valuable
>>>> checkings for the model. But because I have a lot of missing data, I
>>>> could
>>>> not put the residuals into the data. I got errors:
>>>>
>>>> Error in `$<-.data.frame`(`*tmp*`, "resid", value = c(-0.776415  :
>>>> replacement has 13580 rows, data has 68158
>>>>
>>>> Is there any way to match residuals and the predictor?
>>>>
>>>> I tried just plot(model), but it doesn't work for GLMM.
>>>>
>>>>
>>>>
>>>> Thanks!!
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> ----- Original Message ----- 
>>>> From: "Ben Bolker" <bolker at ufl.edu>
>>>> To: <fengsj at mail.utexas.edu>
>>>> Cc: <r-sig-mixed-models at r-project.org>
>>>> Sent: Thursday, June 03, 2010 11:25 AM
>>>> Subject: Re: [R-sig-ME] GLMM linearity checking
>>>>
>>>>
>>>>> fengsj at mail.utexas.edu wrote:
>>>>>> I am sorry for asking this question here. It is more related to
>>>>>> logistic regression.
>>>>>>
>>>>>> I need to use GLMM (binomial(link = "logit")) )to fit my data. The
>>>>>> dependent variable is 0 or 1 and  I'd like to do some roughly
>>>>>> graphical checkings for my data to see if the responses of
>>>>>> transformaed data are linear with respect to continuous predictors in
>>>>>> general. How should I do this?
>>>>>>
>>>>>> Thanks,
>>>>>>
>>>>>   how about
>>>>>
>>>>> m <- glmer(...,data=d)
>>>>> d$resid <- residuals(m)
>>>>>
>>>>> xyplot(resid~continuous_predictor_1,type=c("p","smooth"),data=d)
>>>>>
>>>>> ...
>>>>>
>>>>>  Non-linearity on the transformed scale will appear as a (non-flat)
>>>>> pattern of the (smoothed line fitted to the) residuals as a function 
>>>>> of
>>>>> the continuous predictors ...
>>>>>
>>>>>  Ben Bolker
>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>>
>> -- 
>> Ben Bolker
>> Associate professor, Biology Dep't, Univ. of Florida
>> *** NEW E-MAIL ADDRESSES:
>> ***   bbolker at gmail.com , bolker at math.mcmaster.ca
>> bolker at ufl.edu / people.biology.ufl.edu/bolker
>> GPG key: people.biology.ufl.edu/bolker/benbolker-publickey.asc
>


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