Thanks again,
really appreciate that.
Il giorno 18 Nov 2011, alle ore 23:06, Ben Bolker ha scritto:
> [cc'ing back to r-help]
>
> On Fri, Nov 18, 2011 at 4:39 PM, matteo dossena
> <matteo.doss...@gmail.com> wrote:
>> Thanks a lot,
>>
>> just to make sure i got it right,
>>
>> if (using the real dataset) from the LogLikelihood ratio test model1 isn't
>> "better" than model,
>> means that temporal auto correlation isn't seriously affecting the model?
>
> yes. (or use AIC etc.)
>
>> and, I shouldn't be nesting time within subject because is implicit
>> that observation from the same subject are the repeated measures?
>
> yes.
>
>>
>> The need of nesting would for example be:
>> an experiment also having spatial correlation, to say, if subjects are are
>> also grouped by their geographical position,
>> should i in this case be nesting location within subject?
>>
>
> If subjects were in spatial blocks then you would nest subject
> within location (~1|location/subject).
> Note (from ?corAR1) that you can also use an explicit time covariate,
> (~time|location/subject) -- otherwise
> the assumption is that observations within subject are ordered by time.
>
>> cheers
>> m.
>>
>> Il giorno 18 Nov 2011, alle ore 18:26, Ben Bolker ha scritto:
>>
>>> matteo dossena <matteo.dossena@...> writes:
>>>
>>>>
>>>> Dear list,
>>>>
>>>> I have a data frame like this:
>>>>
>>> set.seed(5)
>>> mydata <- data.frame(var = rnorm(100,20,1),
>>> temp = sin(sort(rep(c(1:10),10))),
>>> subj = as.factor(rep(c(1:10),5)),
>>> time = sort(rep(c(1:10),10)),
>>> trt = rep(c("A","B"), 50))
>>>>
>>>> I need to model the response of var as a function of temp*trt
>>>> and to do so I'm using the following model:
>>>>
>>> library(nlme)
>>> model <- lme(var~temp*trt,random=~1|subj,mydata)
>>>>
>>>> however, since i have repeated measurement of the same subject,
>>>> i.e. I measured var in subj1 at time1,2,3..
>>>> I must consider the non independence of the residuals.
>>>> moreover, temp is also a function of time, but i'm not sure how
>>>> to include this in my model.
>>>>
>>>> I'm following the approach in Zuur et al 2009, so I checked for
>>>> temporal auto-correlation using the
>>>> function afc()
>>>> In fact the residuals follow the temporal patter of temperature with time.
>>>> However, here I'm not interested in the temporal dependence of temperature
>>>> and consequently the effect of
>>>> this on var.
>>>> Instead what i need to do is to account for the
>>>> correlation between consecutive measures (made on the same
>>>> subject) in the error term of the model.
>>>>
>>>> here is my attempt to do so:
>>>>
>>>
>>> model1 <- lme(var~temp*trt,random=~1|subj,
>>> correlation=corAR1(form=~1|subj),mydata)
>>>
>>> model1$modelStruct$corStruct
>>>
>>> Correlation structure of class corAR1 representing
>>> Phi
>>> -0.05565362
>>>
>>> You shouldn't be nesting time within subject. 'subject' is all the
>>> grouping
>>> you need here.
>>>
>>> intervals(model1)
>>>
>>> gives (approximate!!) CI for the correlation structure parameter
>>> (-0.27,0.77) in this case
>>>
>>> Of course, in this case we don't expect to find anything interesting
>>> because these are simulated data without any correlation built in.
>>>
>>> These plots are useful.
>>>
>>> plot(ACF(model),alpha=0.05)
>>> plot(ACF(model1),alpha=0.05) ## should be ALMOST identical to the one above
>>> ## taking correlation into account:
>>> plot(ACF(model1,resType="normalized"),alpha=0.05)
>>>
>>> _______________________________________________
>>> r-sig-mixed-mod...@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
