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09-18-2009 05:07 PM

Hello all!

I am a beginner with SAS (and I am no statistician) working on my DVM (Veterinary Sciences). Using proc mixed for my interval data and proc glimmix for my count data (behaviour study), I have followed the advice of experienced forum members for appropriate covariance structures for my models (see example below for glimmix):

The covariance structure type=sp(pow) time in my glimmix model [repeated date*period/subj=animal(farm) with the subject effect additional to the random farm effect in the random statement] seems to work fine (smaller -2 Res LogL and AIC ....closer to zero, respectively, than with cs);

the same for type=UN@AR(1) for my mixed model;

Still, I face a major problem:

Seemingly, my repeated structure is not correctly modeled as the dimensions table in my output(glimmix) tells me:

Subjects = 1,

max Obs per subject = 234 while I have 26 subjects and 9 observations (3 repetitions per period) per subject

and the model information table states that

variance matrix = not blocked;

Dimensions of G and R-side effects seem to be ok.

similarly, in my output for the mixed model:

Residual variance method = Parameter in the model information and

subject=1, max obs per subject = 234 in the dimensions table;

So what's wrong with my model?

proc glimmix:

proc glimmix data=xxxx;

class genotype farm animal period date;

model x =genotype period genotype*period mean_bg / dist=negbin solution;

random farm animal(farm);

random date(period) / subject=animal(farm) type=sp(pow)(time) residual;

run;

proc mixed:

class genotype farm animal period date;

model x =genotype period genotype*period mean_bg / dist=negbin solution;

random farm animal(farm);

random period date / subject=animal(farm) type=UN@AR(1);

run;

If I omit random farm from the model, the dimensions table looks fine with 26 subjects and max 9 observations per subjects (3 periods and 3 days per period).

I use the univariate format (one row per experimental unit at each timepoint) as emphasized in Littell et al, J. Anim. Sci. 1998. 76:1216–1231 for proc mixed/repeated measurements

and other sources e.g. in http://www.sportsci.org/resource/stats/threetrials.html

I assume it's the same for proc glimmix.

In the SAS glimmix documentation (keyword: processing by subjects) I found the statement that "if a random statement does not have a subject=effect (as I do have in my model: random = farm), processing by subjects is not possible unless the random effect is a pure R-side overdispersion effect".

As far as I understand, processing by subject is a question of how glimmix mathematically processes data but not a question of correctly modeling covariance structures, right ? if not, do I have to choose a subject for farm ???

Anyone a clou about this ?

Another question:

I also want to look at the significance of my random farm effect by omitting it from the model (in mixed as well as in glimmix) above. This worked when I used type=cs but SAS doesn't give a result in the output for the Likelihood Ratio Test if I use the type=sp(pow)(time) which requires the subject effect in the random (G-side effect) statement:

Is the Likelihood Ratio Test not possible with other effects in the random statement (e.g. the subject effect required by UN@AR(1) and sp(pow)(time)) ?

I run out of ideas and would appreciate any advice!

Ka Message was edited by: keckk

I am a beginner with SAS (and I am no statistician) working on my DVM (Veterinary Sciences). Using proc mixed for my interval data and proc glimmix for my count data (behaviour study), I have followed the advice of experienced forum members for appropriate covariance structures for my models (see example below for glimmix):

The covariance structure type=sp(pow) time in my glimmix model [repeated date*period/subj=animal(farm) with the subject effect additional to the random farm effect in the random statement] seems to work fine (smaller -2 Res LogL and AIC ....closer to zero, respectively, than with cs);

the same for type=UN@AR(1) for my mixed model;

Still, I face a major problem:

Seemingly, my repeated structure is not correctly modeled as the dimensions table in my output(glimmix) tells me:

Subjects = 1,

max Obs per subject = 234 while I have 26 subjects and 9 observations (3 repetitions per period) per subject

and the model information table states that

variance matrix = not blocked;

Dimensions of G and R-side effects seem to be ok.

similarly, in my output for the mixed model:

Residual variance method = Parameter in the model information and

subject=1, max obs per subject = 234 in the dimensions table;

So what's wrong with my model?

proc glimmix:

proc glimmix data=xxxx;

class genotype farm animal period date;

model x =genotype period genotype*period mean_bg / dist=negbin solution;

random farm animal(farm);

random date(period) / subject=animal(farm) type=sp(pow)(time) residual;

run;

proc mixed:

class genotype farm animal period date;

model x =genotype period genotype*period mean_bg / dist=negbin solution;

random farm animal(farm);

random period date / subject=animal(farm) type=UN@AR(1);

run;

If I omit random farm from the model, the dimensions table looks fine with 26 subjects and max 9 observations per subjects (3 periods and 3 days per period).

I use the univariate format (one row per experimental unit at each timepoint) as emphasized in Littell et al, J. Anim. Sci. 1998. 76:1216–1231 for proc mixed/repeated measurements

and other sources e.g. in http://www.sportsci.org/resource/stats/threetrials.html

I assume it's the same for proc glimmix.

In the SAS glimmix documentation (keyword: processing by subjects) I found the statement that "if a random statement does not have a subject=effect (as I do have in my model: random = farm), processing by subjects is not possible unless the random effect is a pure R-side overdispersion effect".

As far as I understand, processing by subject is a question of how glimmix mathematically processes data but not a question of correctly modeling covariance structures, right ? if not, do I have to choose a subject for farm ???

Anyone a clou about this ?

Another question:

I also want to look at the significance of my random farm effect by omitting it from the model (in mixed as well as in glimmix) above. This worked when I used type=cs but SAS doesn't give a result in the output for the Likelihood Ratio Test if I use the type=sp(pow)(time) which requires the subject effect in the random (G-side effect) statement:

Is the Likelihood Ratio Test not possible with other effects in the random statement (e.g. the subject effect required by UN@AR(1) and sp(pow)(time)) ?

I run out of ideas and would appreciate any advice!

Ka Message was edited by: keckk