Re: [R] degrees of freedom (lme4 and nlme)
Dear Elizabeth, When I looked for this a couple of years ago, I found DF's to be discussed in the book by Pinheiro Bates Mixed effects models for S and S-Plus, as well as the documentation for SAS's PROC MIXED (I believe that the discussion on df's on the SAS manual was more complete than on the SAS system for mixed models book ---and I think html versions of the manuals for v 8 of SAS can be found on the web). I do not remember specifically, though, whether this discussions mentioned explicitly DFs for fixed effects with crossed random effects (I do not have the references here now). Best, R. On Wednesday 08 September 2004 19:54, Elizabeth Lynch wrote: Hi, I'm looking for pointers/references on calculating den DF's for fixed effects when using crossed random effects. Also, is there an implementation of simulate.lme that I could use in lme4? Thanks, Elizabeth Lynch Douglas Bates wrote: Alexandre Galvão Patriota wrote: Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. SNIP The lme4 package is under development and only has a stub for the code that calculates the denominator degrees of freedom. These Wald-type tests using the F and t distributions are approximations at best. In that sense there is no correct degrees of freedom. I think the more accurate tests may end up being the restricted likelihood ratio tests that Greg Reinsel and his student Mr. Ahn were working on at the time of Greg's death. __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html hthttp://messenger.msn.click-url.com/go/onm00200471ave/direct/01/ __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Ramón Díaz-Uriarte Bioinformatics Unit Centro Nacional de Investigaciones Oncológicas (CNIO) (Spanish National Cancer Center) Melchor Fernández Almagro, 3 28029 Madrid (Spain) Fax: +-34-91-224-6972 Phone: +-34-91-224-6900 http://ligarto.org/rdiaz PGP KeyID: 0xE89B3462 (http://ligarto.org/rdiaz/0xE89B3462.asc) __ [EMAIL PROTECTED] 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] degrees of freedom (lme4 and nlme)
Hi, I'm looking for pointers/references on calculating den DF's for fixed effects when using crossed random effects. Also, is there an implementation of simulate.lme that I could use in lme4? Thanks, Elizabeth Lynch Douglas Bates wrote: Alexandre Galvão Patriota wrote: Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. SNIP The lme4 package is under development and only has a stub for the code that calculates the denominator degrees of freedom. These Wald-type tests using the F and t distributions are approximations at best. In that sense there is no correct degrees of freedom. I think the more accurate tests may end up being the restricted likelihood ratio tests that Greg Reinsel and his student Mr. Ahn were working on at the time of Greg's death. __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html hthttp://messenger.msn.click-url.com/go/onm00200471ave/direct/01/ __ [EMAIL PROTECTED] 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] degrees of freedom (lme4 and nlme)
Alexandre Galvão Patriota wrote: Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method=REML) anova(fm1) numDF DenDF F-valuep-value (Intercept) 1 80 4123.156 .0001 age 1 80114.838 .0001 Sex 1 25 9.2920.0054 The DenDF for each fixed effect is 80, 80 and 25. Using the package lme4: require(lme4) data(Orthodont) fm2-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method=REML) anova(fm2) numDF Sum Sq Mean Sq DenDF F-valuep-value age 1 235.356 235.356 105 114.8382.2e-16 Sex 1 19.044 19.044 1059.292 0.002912 In this case the DenDF for each fixed effect is 105 and 105. In this example, the conclusions are still the same, but it's not the case with another dataset I analyzed. I experience the same type of problem when using glmmPQL of the MASS package and the GLMM of package lme4. Could anyone give me a hint on why the two functions are giving incompatible results? thank you in advance for your help The lme4 package is under development and only has a stub for the code that calculates the denominator degrees of freedom. These Wald-type tests using the F and t distributions are approximations at best. In that sense there is no correct degrees of freedom. I think the more accurate tests may end up being the restricted likelihood ratio tests that Greg Reinsel and his student Mr. Ahn were working on at the time of Greg's death. __ [EMAIL PROTECTED] 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] degrees of freedom (lme4 and nlme)
Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method=REML) anova(fm1) numDF DenDF F-valuep-value (Intercept) 1 80 4123.156 .0001 age 1 80114.838 .0001 Sex 1 25 9.2920.0054 The DenDF for each fixed effect is 80, 80 and 25. Using the package lme4: require(lme4) data(Orthodont) fm2-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method=REML) anova(fm2) numDF Sum Sq Mean Sq DenDF F-valuep-value age 1 235.356 235.356 105 114.8382.2e-16 Sex 1 19.044 19.044 1059.292 0.002912 In this case the DenDF for each fixed effect is 105 and 105. In this example, the conclusions are still the same, but it's not the case with another dataset I analyzed. I experience the same type of problem when using glmmPQL of the MASS package and the GLMM of package lme4. Could anyone give me a hint on why the two functions are giving incompatible results? thank you in advance for your help Alexandre Galvão Patriota. ___ Yahoo! Acesso Grátis - navegue de graça com conexão de qualidade! __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
