'nlme' crashes R (was: Using corStruct in nlme)
Thanks for providing such a self-contained example by which 'nlme' crashes R. Could you please also give us 'sessionInfo()'? I don't have time to test it myself now, but perhaps if you identify your platform, you might interest someone else in checking it. I'm sorry I couldn't be more helpful. Spencer Graves
grieve at u.washington.edu wrote:
I am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are not the same for each 'Plant'.
I have replicated the problem using the CO2 data in nlme (based off of the Ch08.R script).
This works (when 'conc' is the same for each 'Plant': (fm1CO2.lis <- nlsList(SSasympOff, CO2)) (fm1CO2.nlme <- nlme(fm1CO2.lis, control = list(tolerance = 1e-2))) (fm2CO2.nlme <- update(fm1CO2.nlme, random = Asym + lrc ~ 1)) CO2.nlme.var <- update(fm2CO2.nlme, fixed = list(Asym ~ Type * Treatment, lrc + c0 ~ 1), start = c(32.412, 0, 0, 0, -4.5603, 49.344), weights=varConstPower(fixed=list(const=0.1, power=1)), verbose=T) CO2.nlme.CAR<-update(CO2.nlme.var, corr=corCAR1()) CO2.nlme.gauss<-update(CO2.nlme.var, correlation=corGaus(form=~as.numeric(conc)|Plant,nugget=F), data=CO2) CO2.nlme.exp<-update(CO2.nlme.var, correlation=corExp(form=~as.numeric(conc)|Plant,nugget=F), data=CO2) But, if i change each of the 'conc' numbers slightly so that they are no longer
identical between subjects i can only get the corCAR1 correlation to work while R crashes for both corExp and corGaus:
for(i in 1:length(CO2$conc)){
CO2$conc[i]<-(CO2$conc[i]+rnorm(1))
}
(fm1CO2.lis <- nlsList(SSasympOff, CO2))
(fm1CO2.nlme <- nlme(fm1CO2.lis, control = list(tolerance = 1e-2)))
(fm2CO2.nlme <- update(fm1CO2.nlme, random = Asym + lrc ~ 1))
CO2.nlme.var <- update(fm2CO2.nlme,
fixed = list(Asym ~ Type * Treatment, lrc + c0 ~ 1),
start = c(32.412, 0, 0, 0, -4.5603, 49.344),
weights=varConstPower(fixed=list(const=0.1, power=1)), verbose=T)
CO2.nlme.CAR<-update(CO2.nlme.var, corr=corCAR1())
CO2.nlme.gauss<-update(CO2.nlme.var,
correlation=corGaus(form=~as.numeric(conc)|Plant,nugget=F), data=CO2)
CO2.nlme.exp<-update(CO2.nlme.var,
correlation=corExp(form=~as.numeric(conc)|Plant,nugget=F), data=CO2)
I have read Pinheiro & Bates (2000) and i think that it should be possible to fit these correlation structures to my data, but maybe i am mistaken.
I am running R 2.3.1 and have recently updated all packages.
Thanks,
Katie Grieve
Quantitative Ecology & Resource Management
University of Washington
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