topic Random effects for subsampling in RCBD effects model dimensions in SAS Procedures

Random effects for subsampling in RCBD effects model dimensions

mthorne — Fri, 25 Oct 2024 17:55:34 GMT

I am working with a dataset from an agricultural weed management study. The data are counts per sq. meter. There are 2 study sites, 6 treatments, 4 replications per treatment, and 2 subsamples per plot. Plot is the experimental unit and I am considering study site as a random effect. This is a 6-year trial but I have already determined that I need to evaluate each year separately because of an interaction with year sequence and treatment. Using a negative binomial distribution doesn't always work because of zeros resulting from 100% effectiveness of the treatments.

This is the model that seems to work the best and produce dimensions that seem realistic.

ods graphics on;
proc glimmix data=ltf method=laplace plots=studentpanel; by seq;
class site seq trt sub rep;
tctm2 = sqrt(ctm2);
model tctm2 = trt / dist=normal link=identity;
random intercept / subject=rep(sub);
lsmeans trt / ilink diff lines plot=meanplot;
run; quit;
ods graphics off;

It produces this structure. (site=field site, seq=year, trt=treatment, sub=subplot, rep=replication)

Class Level Information
Class	Levels	Values
Site	2	ED ST
Seq	1	6
Trt	6	1 2 3 4 5 6
sub	2	1 2
Rep	4	1 2 3 4

Number of Observations Read	96
Number of Observations Used	96

Dimensions
G-side Cov. Parameters	1
R-side Cov. Parameters	1
Columns in X	7
Columns in Z per Subject	1
Subjects (Blocks in V)	8
Max Obs per Subject	12

If I try to add "site" as a random effect, the model structure seems all wrong, as follows.

ods graphics on;
proc glimmix data=ltf method=laplace plots=studentpanel; by seq;
class site seq trt sub rep;
tctm2 = sqrt(ctm2);
model tctm2 = trt / dist=normal link=identity;
random intercept / subject=site;
random intercept / subject=rep(sub);
lsmeans trt / ilink diff lines plot=meanplot;
run; quit;
ods graphics off;

Class Level Information
Class	Levels	Values
Site	2	ED ST
Seq	1	6
Trt	6	1 2 3 4 5 6
sub	2	1 2
Rep	4	1 2 3 4

Number of Observations Read	96
Number of Observations Used	96

Dimensions
G-side Cov. Parameters	2
R-side Cov. Parameters	1
Columns in X	7
Columns in Z	10
Subjects (Blocks in V)	1
Max Obs per Subject	96

The number of subjects is wrong, and the number of obs per subject is the total number of obs.

Any thoughts on this analysis?

Thanks!

Re: Random effects for subsampling in RCBD effects model dimensions

alexben — Tue, 29 Oct 2024 17:23:48 GMT

@mthorne wrote:
I am working with a dataset from an agricultural weed management study. The data are counts per sq. meter. There are 2 study sites, 6 treatments, 4 replications per treatment, and 2 subsamples per plot. Plot is the experimental unit and I am considering study site as a random effect. This is a 6-year trial but I have already determined that I need to evaluate each year separately because of an interaction with year sequence and treatment. Using a negative binomial distribution doesn't always work because of zeros resulting from 100% effectiveness of the treatments.

This is the model that seems to work the best and produce dimensions that seem realistic.

ods graphics on;
proc glimmix data=ltf method=laplace plots=studentpanel; by seq;
class site seq trt sub rep;
tctm2 = sqrt(ctm2);
model tctm2 = trt / dist=normal link=identity;
random intercept / subject=rep(sub);
lsmeans trt / ilink diff lines plot=meanplot;
run; quit;
ods graphics off;

It produces this structure. (site=field site, seq=year, trt=treatment, sub=subplot, rep=replication)

Class Level InformationClass Levels ValuesSiteSeqTrtsubRep
2 ED ST
1 6
6 1 2 3 4 5 6
2 1 2
4 1 2 3 4

Number of Observations ReadNumber of Observations Used
96
96

DimensionsG-side Cov. ParametersR-side Cov. ParametersColumns in XColumns in Z per SubjectSubjects (Blocks in V)Max Obs per Subject
1
1
7
1
8
12

If I try to add "site" as a random effect, the model structure seems all wrong, as follows.

ods graphics on;
proc glimmix data=ltf method=laplace plots=studentpanel; by seq;
class site seq trt sub rep;
tctm2 = sqrt(ctm2);
model tctm2 = trt / dist=normal link=identity;
random intercept / subject=site;
random intercept / subject=rep(sub);
lsmeans trt / ilink diff lines plot=meanplot;
run; quit;
ods graphics off;

Class Level InformationClass Levels ValuesSiteSeqTrtsubRep
2 ED ST
1 6
6 1 2 3 4 5 6
2 1 2
4 1 2 3 4

Number of Observations ReadNumber of Observations Used
96
96

DimensionsG-side Cov. ParametersR-side Cov. ParametersColumns in XColumns in ZSubjects (Blocks in V)Max Obs per Subject
2
1
7
10
1
96

The number of subjects is wrong, and the number of obs per subject is the total number of obs.

Any thoughts on this analysis?
Thanks!

To properly include site as a random effect in your GLIMMIX model while keeping the structure correct, you can try nesting rep within site, rather than adding a second intercept statement. Here’s a revised model:

sas

Copy code

ods graphics on; proc glimmix data=ltf method=laplace plots=studentpanel; by seq; class site seq trt sub rep; tctm2 = sqrt(ctm2); model tctm2 = trt / dist=normal link=identity; random intercept / subject=site; random intercept / subject=rep(site*sub); lsmeans trt / ilink diff lines plot=meanplot; run; quit; ods graphics off;

This way, you should see more realistic subject structure and parameter estimates. Let me know if this helps!

Re: Random effects for subsampling in RCBD effects model dimensions

mthorne — Tue, 29 Oct 2024 23:56:07 GMT

Thank you so much! This looks good!