[R-sig-ME] GLMM linearity checking

Shujuan Feng fengsj at mail.utexas.edu
Thu Jun 3 19:10:59 CEST 2010

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.


----- 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

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