[R-sig-ME] Specifying correlation structure

[Gang Chen]

Many thanks for the suggestion. It seems that gls does not like that either:

> (fm <- gls(res ~ 1+reg1+reg2+reg3, correlation=corARMA(c(0.02, 0.03), form=~time|condition, p=1,q=1), data=Dat))

Error in model.frame.default(formula = ~time + condition + res + reg1 +  :
  variable lengths differ (found for 'condition')


On Tue, May 15, 2012 at 8:11 AM, Gavin Simpson <gavin.simpson at ucl.ac.uk> wrote:
> On Tue, 2012-05-15 at 06:27 -0400, Gang Chen wrote:
>> I have some time series data (with 100 time points) collected from one
>> subject. At each time point three measures were taken under three
>> conditions, and there is an explanatory variable for each condition.
>
> The temporal correlation is, in this sense, *nested* within the three
> different conditions. You were close with time:condition, but what I
> think you need is
>
> corARMA(c(0.02, 0.03), form = ~ time | condition)
>
> HTH
>
> G
>
>> Below is just some fake data to demonstrate the data structure:
>>
>> # 100 time points per condition
>> tp <- 100                             # time points
>> trials <- seq(1, tp, 1)          # time counter
>> condition <- c('a', 'b', 'c')   # three conditions
>>
>> # fake data
>> set.seed(5)
>> Dat <- data.frame(time=rep(trials, 3), cond=rep(condition, each=tp),
>> res=rnorm(3*tp))
>>
>> reg1 <- c(rnorm(tp), rep(0, 2*tp))            # explanatory variable
>> for condition 1
>> reg2 <- c(rep(0, tp), rnorm(tp), rep(0, tp))   # explanatory variable
>> for condition 2
>> reg3 <- c(rep(0, 2*tp), rnorm(tp))             # explanatory variable
>> for condition 3
>>
>> First I thought that I'd start with gls in nlme package with an ARMA
>> model for the correlation structure:
>>
>> (fm <- gls(res ~ 1+reg1+reg2+reg3, correlation=corARMA(c(0.02, 0.03),
>> form=~time, p=1,q=1), data=Dat))
>>
>> The above model does not work because of the following error:
>>
>> Error in Initialize.corARMA(X[[1L]], ...) :
>>   Covariate must have unique values within groups for corARMA objects
>>
>> Moreover, I would like to account for the fact that the ARMA structure
>> should be similar or the same across the three conditions. My
>> questions are:
>>
>> 1) how to impose a same ARMA structure across the three conditions?
>> 2) alternatively I'd like to have something like:
>>
>> (fm <- gls(res ~ 1+reg1+reg2+reg3, correlation=corARMA(c(0.02, 0.03),
>> form=~time:condition, p=1,q=1), data=Dat))
>>
>> but this would not work either, and seems to crash R!
>>
>> 3) maybe construct a multivariate gls model, but how to do that? what package?
>>
>> Thanks,
>> Gang
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
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