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-value p-value
(Intercept) 1 80 4123.156 <.0001
age 1 80 114.838 <.0001
Sex 1 25 9.292 0.0054
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-value p-value
age 1 235.356 235.356 105 114.838 <2.2e-16
Sex 1 19.044 19.044 105 9.292 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 Galv??o Patriota.
_______________________________________________________
Alexandre Galv??o 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-value p-value > (Intercept) 1 80 4123.156 <.0001 > age 1 80 114.838 <.0001 > Sex 1 25 9.292 0.0054 > > > 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-value p-value > age 1 235.356 235.356 105 114.838 <2.2e-16 > Sex 1 19.044 19.044 105 9.292 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 helpThe 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.
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. > >______________________________________________ >R-help at 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