'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