Dear Gavin,
It SHOULD work if you add
random = ~log.so4+log.cl|site
I made a script which is able to do so, but perhaps the addition of this extra variability in your case over-specifies the model?
Hope this helps. Otherwise I suppose that you have to supply more details.
Cheers,
Fredrik
--------------------------------------------------------------------------
Example:
nsite<-10
nwithin<-10
prosw<-nsite*nwithin
conc<-vector(length=prosw)
temp<-rnorm(prosw)
log.so4<-seq(-5,5, length=nwithin)
log.cl<-rnorm(prosw)
int<-17
acl<-sqrt(2)
aso4<-21
atemp<- -14
rho<--0.9
for (i in 1:nsite)
{
sf<-0.1*rnorm(1)
sc<-2*rnorm(1)
ss<-rnorm(1)
err<-0.1*rnorm(1)
for (j in 1:nwithin)
{
conc[nwithin*(i-1)+j]<-sf + int + (acl+sc)*log.cl[nwithin*(i-1)+j] + (aso4+ss)*log.so4[j] +
atemp*temp[nwithin*(i-1)+j] + err
err<-err*rho + 0.1*rnorm(1)
}
}
temp<-rep(temp,nsite)
log.so4<-rep(log.so4, nsite)
log.cl<-rep(log.cl,nsite)
site<-rep(seq(1,nsite), each=nwithin)
DF<-data.frame(conc,temp, log.so4, log.cl, site)
DF$site<-as.factor(site)
DFgd<-groupedData(conc~temp|site, data=DF)
plot(DFgd)
DF.lme<-lme(conc~log.so4+temp+log.cl, random=~log.so4+log.cl|site, data=DF)
plot(ACF(DF.lme, resType="n", form=~1|site, maxLag=floor(nwithin/2-.5)), alpha=0.05)
DF2.lme<-update(DF.lme, correlation=corAR1(form=~1|site))
-----Ursprungligt meddelande-----
Fr?n: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] F?r Gavin Simpson
Skickat: den 9 maj 2007 15:24
Till: R-SIG-Mixed-Model
?mne: [R-sig-ME] Specifying random effects in lme()
Dear List,
I am modelling repeated measures data from 11 sites in the UK using
lme() from the nlme package. I have the following call for a random
intercept model with an AR1 error structure:
mod <- lme(doc ~ temp + log.so4 + log.cl, random = ~ 1 | site,
correlation = corAR1(), data = hydro)
I would like to fit a model that has random effects for log.so4 and
log.cl within site, so that I am fitting a random slop and intercept
model --- we want the effect of log.so4 and log.cl to vary between sites
and to compare the fits of the two models.
If I am following lmer() in lme4 correctly, I believe I could fit this
model as:
hydro.lmer <- lmer(doc ~ temp + log.so4 + log.cl +
(log.so4 | site) + (log.cl | site),
data = hydro)
but without the AR1 correlation.
I am struggling to get the syntax correct for random in lme() to add
random effects to log.so4 and log.cl. I have only managed to get a model
where one of the coefficients (log.cl) also has a random effect.
I would be most grateful if someone could explain how to fit the model
in lme() by showing me the correct form for the random argument.
Many thanks,
Gav