Re: [R] R package that has (much) the same capabilities as SAS v9 PROC GENMOD
On Tue, 5 Apr 2005, Simon Blomberg wrote: The questioner clearly wants generalized linear mixed models. lmer in package lme4 may be more appropriate. (Prof. Bates is a co-author.). glmmPQL should do the same job, though, but with less accuracy. Actually, I think the questioner wants GEE, from geepack or yags. SAS has an excellent glmm implementation, but it's through PROC NLMIXED rather than GENMOD, which does marginal models. -thomas Simon. check glm() On Apr 4, 2005 6:46 PM, William M. Grove [EMAIL PROTECTED] wrote: I need capabilities, for my data analysis, like the Pinheiro Bates S-Plus/R package nlme() but with binomial family and logit link. I need multiple crossed, possibly interacting fixed effects (age cohort of twin when entered study, sex of twin, sampling method used to acquire twin pair, and twin zygosity), a couple of random effects other than the cluster variable, and the ability to have a variable of the sort that PB call outer to the clustering variable---zygosity. Dependent variables are all parental (mom, dad separately of course) psychiatric diagnoses. In my data, twin pair ID is the clustering variable; correlations are expected to be exchangeable but substantially different between members of monozygotic twin pairs and members of dizygotic twin pairs. Hence, in my analyses, the variable that's outer to twin pair is monozygotic vs. dizygotic which of course applies to the whole pair. nlme() does all that but requires quasi-continuous responses, according to the preface/intro of PB's mixed models book and what I infer from online help (i.e., no family= or link= argument). The repeated() library by Lindsey seems to handle just one nested random effect, or so I believe I read while scanning backlogs of the R-Help list. glmmPQL() is in the ballpark of what I need, but once again seems to lack the outer variable specification that nlme() has, and which PROC GENMOD also has---and which I need. I read someplace of yags() that apparently uses GEE to estimate parameters of nonlinear models including GLIMs/mixed models, just the way PROC GENMOD (and many another program) does. But on trying to install it (either v4.0-1.zip or v4.0-2.tar.gz from Carey's site, or Ripley's Windows port) from a local, downloaded zip file (or tar.gz file converted to zip file), I always get an error saying: Error in file(file, r) : unable to open connection In addition: Warning message: cannot open file `YAGS/DESCRIPTION' with no obvious solution. So I can't really try it out to see if it does what I want. You may ask: Why not just use GENMOD and skip the R hassles? Because I want to embed the GLIM/mixed model analysis in a stratified resampling bootstrapping loop. Very easy to implement in R, moderately painful to do in SAS. Can anybody give me a lead, or some guidance, about getting this job done in R? Thanks in advance for your help. Regards, Will Grove | Iohannes Paulus PP. II, xxx Psychology Dept. | U. of Minnesota | -+ X-headers have PGP key info.; Call me at 612.625.1599 to verify key fingerprint before accepting signed mail as authentic! br x-sigsepp/x-sigsep Will Grovenbsp;nbsp;nbsp;nbsp;nbsp;nbsp; | Iohannes Paulus PP. II, xxx br Psychology Dept. |br U. of Minnesotanbsp; |br -+br br X-headers have PGP key info.; Call me at 612.625.1599 to verify key fingerprintbr before accepting signed mail as authentic!br br /body /html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- WenSui Liu, MS MA Senior Decision Support Analyst Division of Health Policy and Clinical Effectiveness Cincinnati Children Hospital Medical Center __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Simon Blomberg, B.Sc.(Hons.), Ph.D, M.App.Stat. Visiting Fellow School of Botany Zoology The Australian National University Canberra ACT 0200 Australia T: +61 2 6125 8057 email: [EMAIL PROTECTED] F: +61 2 6125 5573 CRICOS Provider # 00120C __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Thomas Lumley Assoc. Professor, Biostatistics [EMAIL PROTECTED] University of Washington, Seattle __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] R package that has (much) the same capabilities as SAS v9 PROC GENMOD
I need capabilities, for my data analysis, like the Pinheiro Bates S-Plus/R package nlme() but with binomial family and logit link. I need multiple crossed, possibly interacting fixed effects (age cohort of twin when entered study, sex of twin, sampling method used to acquire twin pair, and twin zygosity), a couple of random effects other than the cluster variable, and the ability to have a variable of the sort that PB call outer to the clustering variable---zygosity. Dependent variables are all parental (mom, dad separately of course) psychiatric diagnoses. In my data, twin pair ID is the clustering variable; correlations are expected to be exchangeable but substantially different between members of monozygotic twin pairs and members of dizygotic twin pairs. Hence, in my analyses, the variable that's outer to twin pair is monozygotic vs. dizygotic which of course applies to the whole pair. nlme() does all that but requires quasi-continuous responses, according to the preface/intro of PB's mixed models book and what I infer from online help (i.e., no family= or link= argument). The repeated() library by Lindsey seems to handle just one nested random effect, or so I believe I read while scanning backlogs of the R-Help list. glmmPQL() is in the ballpark of what I need, but once again seems to lack the outer variable specification that nlme() has, and which PROC GENMOD also has---and which I need. I read someplace of yags() that apparently uses GEE to estimate parameters of nonlinear models including GLIMs/mixed models, just the way PROC GENMOD (and many another program) does. But on trying to install it (either v4.0-1.zip or v4.0-2.tar.gz from Carey's site, or Ripley's Windows port) from a local, downloaded zip file (or tar.gz file converted to zip file), I always get an error saying: Error in file(file, r) : unable to open connection In addition: Warning message: cannot open file `YAGS/DESCRIPTION' with no obvious solution. So I can't really try it out to see if it does what I want. You may ask: Why not just use GENMOD and skip the R hassles? Because I want to embed the GLIM/mixed model analysis in a stratified resampling bootstrapping loop. Very easy to implement in R, moderately painful to do in SAS. Can anybody give me a lead, or some guidance, about getting this job done in R? Thanks in advance for your help. Regards, Will Grove | Iohannes Paulus PP. II, xxx Psychology Dept. | U. of Minnesota | -+ X-headers have PGP key info.; Call me at 612.625.1599 to verify key fingerprint before accepting signed mail as authentic! br x-sigsepp/x-sigsep Will Grovenbsp;nbsp;nbsp;nbsp;nbsp;nbsp; | Iohannes Paulus PP. II, xxx br Psychology Dept. |br U. of Minnesotanbsp; |br -+br br X-headers have PGP key info.; Call me at 612.625.1599 to verify key fingerprintbr before accepting signed mail as authentic!br br /body /html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] R package that has (much) the same capabilities as SAS v9 PROC GENMOD
The questioner clearly wants generalized linear mixed models. lmer in package lme4 may be more appropriate. (Prof. Bates is a co-author.). glmmPQL should do the same job, though, but with less accuracy. Simon. check glm() On Apr 4, 2005 6:46 PM, William M. Grove [EMAIL PROTECTED] wrote: I need capabilities, for my data analysis, like the Pinheiro Bates S-Plus/R package nlme() but with binomial family and logit link. I need multiple crossed, possibly interacting fixed effects (age cohort of twin when entered study, sex of twin, sampling method used to acquire twin pair, and twin zygosity), a couple of random effects other than the cluster variable, and the ability to have a variable of the sort that PB call outer to the clustering variable---zygosity. Dependent variables are all parental (mom, dad separately of course) psychiatric diagnoses. In my data, twin pair ID is the clustering variable; correlations are expected to be exchangeable but substantially different between members of monozygotic twin pairs and members of dizygotic twin pairs. Hence, in my analyses, the variable that's outer to twin pair is monozygotic vs. dizygotic which of course applies to the whole pair. nlme() does all that but requires quasi-continuous responses, according to the preface/intro of PB's mixed models book and what I infer from online help (i.e., no family= or link= argument). The repeated() library by Lindsey seems to handle just one nested random effect, or so I believe I read while scanning backlogs of the R-Help list. glmmPQL() is in the ballpark of what I need, but once again seems to lack the outer variable specification that nlme() has, and which PROC GENMOD also has---and which I need. I read someplace of yags() that apparently uses GEE to estimate parameters of nonlinear models including GLIMs/mixed models, just the way PROC GENMOD (and many another program) does. But on trying to install it (either v4.0-1.zip or v4.0-2.tar.gz from Carey's site, or Ripley's Windows port) from a local, downloaded zip file (or tar.gz file converted to zip file), I always get an error saying: Error in file(file, r) : unable to open connection In addition: Warning message: cannot open file `YAGS/DESCRIPTION' with no obvious solution. So I can't really try it out to see if it does what I want. You may ask: Why not just use GENMOD and skip the R hassles? Because I want to embed the GLIM/mixed model analysis in a stratified resampling bootstrapping loop. Very easy to implement in R, moderately painful to do in SAS. Can anybody give me a lead, or some guidance, about getting this job done in R? Thanks in advance for your help. Regards, Will Grove | Iohannes Paulus PP. II, xxx Psychology Dept. | U. of Minnesota | -+ X-headers have PGP key info.; Call me at 612.625.1599 to verify key fingerprint before accepting signed mail as authentic! br x-sigsepp/x-sigsep Will Grovenbsp;nbsp;nbsp;nbsp;nbsp;nbsp; | Iohannes Paulus PP. II, xxx br Psychology Dept. |br U. of Minnesotanbsp; |br -+br br X-headers have PGP key info.; Call me at 612.625.1599 to verify key fingerprintbr before accepting signed mail as authentic!br br /body /html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- WenSui Liu, MS MA Senior Decision Support Analyst Division of Health Policy and Clinical Effectiveness Cincinnati Children Hospital Medical Center __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Simon Blomberg, B.Sc.(Hons.), Ph.D, M.App.Stat. Visiting Fellow School of Botany Zoology The Australian National University Canberra ACT 0200 Australia T: +61 2 6125 8057 email: [EMAIL PROTECTED] F: +61 2 6125 5573 CRICOS Provider # 00120C __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
