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: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > On > Behalf Of Andreas Nord > Sent: Monday, August 25, 2008 9:22 AM > To: r-help@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. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/lmer4-and-variable-selection-tp19146850p19152509.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
