Covariance structure specification
Angelina Mukherjee <angelina.mukherjee88 at ...> writes:
I have response measures corresponding to 2 patients. The structure is as
follows:
Patient 1: Region 1 Region 2 Region 3
S1 S2 S3 S4 S1 S2 S3 S4 S1 S2 S3 S4
Patient 2: Region 1 Region 2 Region 3
S1 S2 S3 S4 S1 S2 S3 S4 S1 S2 S3 S4
Hmm. Do you really have only two patients, i.e. a total of 24 response values? I understand that you're trying to do a variance decomposition here (no fixed effects, only random effects), but your estimates of variance will be extremely inaccurate based on only two patients (you might want to consider making patient a fixed effect, then you would at least have 6 data points (5 df) for the patient:region variance ...
Each patient has 3 regions and each region has 4 sub-regions. (nested design) Fitting *lme( Response ~ 1, random=~ Patient + Region +Subregion | Patient/Region/Subregion )* allows me to specify covariance structure for the 'sub-region' term. But I'm trying to fit a random effects model of the form as I have only 1 observation per 'sub-region': *lme( Response ~ 1, random=~ Patient + Region | Patient/Region )* Is there a way I can specify a covariance structure like the auto-regressive (to specify that correlation decreases with distance as one moves from Subregion 1 to Subregion 4) for the 'sub-region' term only as it is not included in my random effects model but I'd like to account for the correlation in it?
I would think that something like lme(Response~1, random = ~1|Patient/Region, correlation=corAR1(~Subregion)) But I also think you're fitting a more complicated model than can really be supported by 24 data points ... Ben Bolker