#short example version of input file with 2 runs and 5 time steps (instead
of 17 runs and 500 time steps)
run t cover
1 1 0.234306
1 2 0.188896
1 3 0.198193
1 4 0.213959
1 5 0.184952
2 1 0.189316
2 2 0.185631
2 3 0.20211
2 4 0.216064
2 5 0.216064
#calculate the correlation of lag 1 over 17 replicates
a<-0
for (i in 1:17)
{
c<-ts( cover[run==i] )
d<-acf( c, lag=1, plot=F)
a<-a+d$acf[2]
}
a<-a/17
a
#[1] 0.9021463
#mixed effects model
model1<-lme(cover~t,random=~t|run, method="ML")
model2<-update(model1,correlation=corCAR1(0.902,form=~t|run))
anova(model1,model2)
But this just gives significance for a lag of 1, so I tried to find out the
correlation at greater lags with arima to be able to use corARMA() as
correlation structure:
arima(cover[run==1],order=c(100,0,0))
#does not work: “error in polyroot(z): polynomial degree too high”
Any ideas to solve this? Maybe, I don’t even need a mixed effects model?
I would be very grateful for any help
Katrin
--
Katrin Meyer
Institute of Ecology
Friedrich-Schiller-University
Dornburger Str. 159
D- 07743 Jena
Germany
"Feel free" mit GMX FreeMail!
Monat für Monat 10 FreeSMS inklusive! http://www.gmx.net