Re: [R] lmer4 and variable selection

From: Andreas Nord <andreas.nord_at_zooekol.lu.se>
Date: Mon, 25 Aug 2008 15:03:23 -0700 (PDT)

Bert,

Yes, I am aware of that, maybe I was a bit unclear in my previous post. My issue is not at all with interpreting data or deciding on what to do with it. That I am quite capable of doing myself and I could perform the analyses I want to do using other software. However, as I'm trying to implement R in my own research (and at the deparment as well!) I'd prefer to perform them using lmer.

Thus, I simply wanted advice on how to apply a simple variable selection method using the lmer-output. Of course, I could drop or add variables at random and do the selection "manually", but I'm not too keen on doing that with such a large original model. I hope this clarified my issue a bit!

All the best,
Andreas

Bert Gunter wrote:
>
>
> You **really** should work with a local statistician. Remote statistical
> advice (this is not really about R) from even well-meaning helpers
> unfamiliar with your work is really very risky. For example, I would
> suggest
> making all sorts of plots (statistical summaries alone are wholly
> inadequate
> and potentially quite misleading), but exactly what to plot, how to
> interpret what the plots show, and what to do next would depend on both
> the
> subject matter background (how the study was conducted and what sorts of
> mechanisms are expected, for example)and what the plots revealed.
>
> Like the gangster movies (used to) say: just a friendly warning ... :)
>
> -- Bert Gunter
> Genentech
>
>
> ----- Original Message-----
> From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org]
> On
> Behalf Of Andreas Nord
> Sent: Monday, August 25, 2008 9:22 AM
> To: r-help_at_r-project.org
> Subject: [R] lmer4 and variable selection
>
>
> Dear list,
>
> I am currently working with a rather large data set on body temperature
> regulation in wintering birds. My original model contains quite a few
> dependent variables, but I do not (of course) wish to keep them all in my
> final model. I've fitted the following model to the data:
>

>>

> temp.lme1<-lmer(T.B~tarsus+wing+weight+factor(age)+factor(sex)+fat+minsunset
> +day1oct+day1oct.2+minnight+ave.day+minnight.1+T.A+ave.night.1+(1|ID)+(1|sig
> n),data=bodytemp.df)
>
> where T.B equals body temperature; explanatories are a number of biometric
> measures (tarsus, wing, weight, fat, age, sex) and various measures of
> ambient temperature (ave.day, minnight.1, minnight, ave.night.1, T.A) and
> time/date (minsunset,day1oct,day1oct.2). Random factors are ID
> (individuals
> were samples ranging from 1 to 3 times) and sign (person performing
> measurements; 2 levels).
>
> Model output looks like this:
>
>> summary(temp.lme1)

> Linear mixed model fit by REML
> Formula: T.B ~ tarsus + wing + weight + factor(age) + factor(sex) + fat +
>
> minsunset + day1oct + day1oct.2 + minnight + ave.day + minnight.1 +
> T.A
> + ave.night.1 + (1 | ID) + (1 | sign)
> Data: bodytemp.df
> AIC BIC logLik deviance REMLdev
> 557.8 614 -260.9 441 521.8
> Random effects:
> Groups Name Variance Std.Dev.
> ID (Intercept) 1.0399e-01 0.32247096
> sign (Intercept) 6.2663e-08 0.00025033
> Residual 8.0162e-01 0.89533134
> Number of obs: 167, groups: ID, 124; sign, 2
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 4.124e+01 4.104e+00 10.049
> tarsus -5.925e-02 5.801e-02 -1.021
> wing -6.252e-02 4.984e-02 -1.254
> weight 1.499e-01 1.446e-01 1.037
> factor(age)2K+ 1.981e-01 1.651e-01 1.200
> factor(sex)M 9.232e-02 2.146e-01 0.430
> fat -2.297e-02 8.150e-02 -0.282
> minsunset -1.104e-03 1.043e-03 -1.058
> day1oct -4.247e-03 2.879e-02 -0.148
> day1oct.2 5.087e-05 1.560e-04 0.326
> minnight -5.987e-02 7.022e-02 -0.853
> ave.day 1.128e-01 1.582e-01 0.713
> minnight.1 -9.590e-02 1.684e-01 -0.570
> T.A -4.855e-02 5.185e-02 -0.936
> ave.night.1 1.420e-01 2.477e-01 0.573
>
> Correlation of Fixed Effects:
> (Intr) tarsus wing weight f()2K+ fct()M fat mnsnst day1ct
> dy1c.2 mnnght ave.dy mnng.1 T.A
> tarsus -0.851
>
> wing -0.870 0.966
>
> weight 0.071 -0.417 -0.411
>
> factr(g)2K+ 0.211 -0.248 -0.241 0.219
>
> factor(sx)M 0.573 -0.499 -0.526 -0.179 0.105
>
> fat -0.037 0.046 0.052 -0.264 -0.152 0.045
>
> minsunset -0.177 -0.144 -0.122 0.214 -0.101 -0.027 -0.045
>
> day1oct -0.261 -0.051 -0.052 -0.117 -0.145 0.140 0.131 0.515
>
> day1oct.2 0.257 0.050 0.051 0.121 0.141 -0.149 -0.125 -0.484 -0.993
>
> minnight -0.074 0.249 0.216 -0.271 -0.032 -0.043 0.022 0.022 -0.168
> 0.231
> ave.day -0.025 0.070 0.050 0.001 0.045 -0.022 0.046 -0.363 -0.120
> 0.041 -0.415
> minnight.1 0.304 -0.081 -0.045 0.069 0.129 0.012 -0.054 -0.349 -0.636
> 0.644 0.023 0.052
> T.A 0.049 -0.043 0.018 0.130 0.040 -0.164 -0.065 -0.317 -0.288
> 0.249 -0.598 0.267 0.143
> ave.night.1 -0.234 0.004 -0.015 -0.030 -0.110 0.016 0.031 0.493 0.614
> -0.586 0.105 -0.524 -0.863 -0.243
>
> At this point, I want to go on selecting the variables with most
> explanatory
> power to come up with a final model. However, I'm not sure on how to do
> this, because (not being a trained statistician) I'm used to having
> p-values
> to guide me. Similarly, I would like to be able to report the relative
> "importance" of variables in some way but, as apparent from a number of
> threads, p-values seem to be the least preferred option when it comes to
> lmer. I've read about the mcmcsamp()-function, but I'm not entirely sure
> on
> how to use it or on how to intrepret the output.
>
> Any advice would be most appreciated.
>
>
> Kind regards,
> Andreas Nord
>
> --
> View this message in context:
> http://www.nabble.com/lmer4-and-variable-selection-tp19146850p19146850.html
> Sent from the R help mailing list archive at Nabble.com.
>
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> ______________________________________________
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> PLEASE do read the posting guide
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>
>
-- 
View this message in context: http://www.nabble.com/lmer4-and-variable-selection-tp19146850p19152509.html
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