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03-31-2023
Lesley6145
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Latest posts by Lesley6145
Subject Views Posted 1055 01-20-2023 08:44 PM 1185 01-17-2023 07:58 PM -
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- Posted Re: GLIMMIX for count data in staggered-start design on Statistical Procedures. 01-20-2023 08:44 PM
- Posted GLIMMIX for count data in staggered-start design on Statistical Procedures. 01-17-2023 07:58 PM
01-20-2023
08:44 PM
Thank you for your response. Yes, indeed there are three fixed factors (Herb, Veg, and Time-since-treatment). Regarding clarification: The plots number from 1-24 within each site corresponding to the 2 Herb x 3 Veg x 4 replicate combinations; these combinations were randomly assigned within each site so plot numbers do not necessarily correspond to the same treatment combinations. The same plot number in a site does correspond to counts for that plot in year 2 and year 3. I think that is consistent with what you mean by “identical within site from year to year”. In running your recommended code, it is making some sense to me how you rearranged the random statements. The default pseudo-likelihood method of optimization however does not generate AICC so I am unable to conduct model selection (i.e., comparison of full model herb|veg|time versus herb|time, herb time, veg|time, etc.) to rank plausible models. Is there a rationale why Laplace integration is inappropriate here? It does allow model selection in this fashion, but with your new code Laplace provides F-stats/Pvalues for all terms except those with Time, and the error “Estimated G matrix is not positive definite”. In addition to this set of models, I am also trying to include models that contain covariates to serve in lieu of the random effects of year, year*herb, and year*veg. The covariate “shrub” is one such covariate that may influence seedling counts, and it was measured on each plot in each year. So, for example: count = herb veg shrub*herb shrub*veg time herb*time veg*time herb*veg*time With year effects now substituted by the shrub covariate, I assume I only maintain the “random intercept plot / subject=site;” for this type of model?
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01-17-2023
07:58 PM
Hi, I'm new to this forum, but have used SAS for a number of years, and I am seeking guidance on proc GLIMMX. I'm testing how well plants establish after herbicide treatment (herb) and seeding methods (seed) applied across a disturbed landscape (fixed effects). The design is a staggered start experiment so that time-since-treatment effect (fixed factor) can be separated from year effects (random factor) as follows: -- Herbicide (2 levels) x seeding (3 levels) treatment combinations were randomly assigned to 24 plots (4 replicates per treatment combination) in each of 10 sites (blocking factor) --The plots were treated with herbicide and seeding treatments across 3 successive years (i.e., plots were treated at 4 sites in 2018, at another 3 sites in 2019, and another 3 in 2020). -- Seedlings were enumerated on plots (counts) for 2 years after treatments were implemented (e.g., plots treated in 2018 were measured in 2019 and 2020; plots treated in 2019 measured in 2020 and 2021, and so on for 2020 plots) --There is also a covariate (Covar) that is a count of neighbor shrubs, measured on each plot in each year. It will be relevant for my second question. I am using GLIMMIX to analyze herbicide, seeding, and time-since-treated and their interactions. I’m trying to estimate variances for the random effects, and as I am more familiar with MIXED, I’m unsure if I have the code written properly. data exper1;
input Time Year Site Herb$ Veg$ Plot Count Covar;
datalines;
2 2018 1 no A 21 3 3
2 2018 1 no A 25 12 5
2 2018 1 no A 29 2 0
2 2018 1 no A 32 1 1
2 2018 1 no B 19 2 2
2 2018 1 no B 22 10 13
2 2018 1 no B 23 6 1
2 2018 1 no B 30 6 7
2 2018 1 no C 24 4 7
2 2018 1 no C 26 4 4
2 2018 1 no C 28 6 3
2 2018 1 no C 31 3 3
2 2018 1 yes A 5 0 5
2 2018 1 yes A 6 7 3
2 2018 1 yes A 7 3 1
2 2018 1 yes A 10 1 4
2 2018 1 yes B 3 6 3
2 2018 1 yes B 4 2 6
2 2018 1 yes B 12 1 3
2 2018 1 yes B 16 1 5
2 2018 1 yes C 2 5 7
2 2018 1 yes C 9 5 6
2 2018 1 yes C 14 9 10
2 2018 1 yes C 15 14 6
2 2020 2 no A 20 0 33
2 2020 2 no A 21 0 34
2 2020 2 no A 29 0 16
2 2020 2 no A 32 0 47
2 2020 2 no B 23 0 24
2 2020 2 no B 24 0 31
2 2020 2 no B 28 0 28
2 2020 2 no B 31 0 37
2 2020 2 no C 17 0 25
2 2020 2 no C 18 1 20
2 2020 2 no C 22 0 24
2 2020 2 no C 26 0 28
2 2020 2 yes A 10 0 24
2 2020 2 yes A 12 0 24
2 2020 2 yes A 13 0 8
2 2020 2 yes A 15 0 5
2 2020 2 yes B 3 3 35
2 2020 2 yes B 6 0 25
2 2020 2 yes B 9 0 44
2 2020 2 yes B 14 0 40
2 2020 2 yes C 2 0 50
2 2020 2 yes C 8 0 26
2 2020 2 yes C 11 0 14
2 2020 2 yes C 16 0 5
2 2018 3 no A 17 1 2
2 2018 3 no A 24 1 0
2 2018 3 no A 25 5 4
2 2018 3 no A 32 3 3
2 2018 3 no B 18 2 5
2 2018 3 no B 29 1 0
2 2018 3 no B 30 0 5
2 2018 3 no B 31 0 0
2 2018 3 no C 20 1 6
2 2018 3 no C 23 0 3
2 2018 3 no C 27 0 0
2 2018 3 no C 28 0 0
2 2018 3 yes A 4 2 5
2 2018 3 yes A 14 1 0
2 2018 3 yes A 15 2 2
2 2018 3 yes A 16 0 0
2 2018 3 yes B 5 0 1
2 2018 3 yes B 9 0 0
2 2018 3 yes B 12 1 1
2 2018 3 yes B 13 0 0
2 2018 3 yes C 1 9 5
2 2018 3 yes C 7 2 0
2 2018 3 yes C 8 0 0
2 2018 3 yes C 10 1 3
2 2018 4 no A 17 1 21
2 2018 4 no A 20 5 24
2 2018 4 no A 26 0 22
2 2018 4 no A 27 4 27
2 2018 4 no B 21 27 31
2 2018 4 no B 28 0 23
2 2018 4 no B 31 2 32
2 2018 4 no B 32 28 23
2 2018 4 no C 19 25 19
2 2018 4 no C 22 3 25
2 2018 4 no C 25 1 39
2 2018 4 no C 29 3 30
2 2018 4 yes A 3 47 51
2 2018 4 yes A 10 6 23
2 2018 4 yes A 13 7 41
2 2018 4 yes A 14 0 25
2 2018 4 yes B 6 3 26
2 2018 4 yes B 8 1 32
2 2018 4 yes B 12 19 28
2 2018 4 yes B 15 0 32
2 2018 4 yes C 1 1 12
2 2018 4 yes C 2 3 39
2 2018 4 yes C 5 1 36
2 2018 4 yes C 9 1 36
2 2019 5 no A 18 0 10
2 2019 5 no A 23 5 21
2 2019 5 no A 27 12 26
2 2019 5 no A 31 0 11
2 2019 5 no B 17 0 26
2 2019 5 no B 21 0 21
2 2019 5 no B 28 1 14
2 2019 5 no B 29 2 13
2 2019 5 no C 19 1 8
2 2019 5 no C 20 0 17
2 2019 5 no C 22 4 6
2 2019 5 no C 24 4 16
2 2019 5 yes A 1 0 14
2 2019 5 yes A 6 1 21
2 2019 5 yes A 10 0 9
2 2019 5 yes A 14 2 4
2 2019 5 yes B 3 0 14
2 2019 5 yes B 11 1 13
2 2019 5 yes B 12 3 6
2 2019 5 yes B 16 2 7
2 2019 5 yes C 2 1 7
2 2019 5 yes C 4 2 8
2 2019 5 yes C 7 1 7
2 2019 5 yes C 9 0 7
2 2020 6 no A 23 3 32
2 2020 6 no A 24 41 35
2 2020 6 no A 29 2 4
2 2020 6 no A 31 0 6
2 2020 6 no B 17 0 0
2 2020 6 no B 21 0 24
2 2020 6 no B 27 16 21
2 2020 6 no B 30 3 56
2 2020 6 no C 22 15 40
2 2020 6 no C 26 18 38
2 2020 6 no C 28 2 43
2 2020 6 no C 32 9 41
2 2020 6 yes A 9 0 68
2 2020 6 yes A 10 7 50
2 2020 6 yes A 15 0 22
2 2020 6 yes A 16 0 25
2 2020 6 yes B 4 0 60
2 2020 6 yes B 5 2 34
2 2020 6 yes B 7 0 38
2 2020 6 yes B 11 20 39
2 2020 6 yes C 1 0 23
2 2020 6 yes C 3 0 36
2 2020 6 yes C 8 1 47
2 2020 6 yes C 13 3 54
2 2019 7 no A 18 0 44
2 2019 7 no A 25 2 38
2 2019 7 no A 26 0 33
2 2019 7 no A 32 0 32
2 2019 7 no B 23 0 17
2 2019 7 no B 24 0 64
2 2019 7 no B 27 5 43
2 2019 7 no B 28 1 38
2 2019 7 no C 17 1 28
2 2019 7 no C 19 0 35
2 2019 7 no C 20 10 37
2 2019 7 no C 29 5 31
2 2019 7 yes A 12 0 21
2 2019 7 yes A 13 3 21
2 2019 7 yes A 15 2 47
2 2019 7 yes A 16 2 50
2 2019 7 yes B 1 1 50
2 2019 7 yes B 2 4 43
2 2019 7 yes B 3 0 49
2 2019 7 yes B 8 0 57
2 2019 7 yes C 4 1 63
2 2019 7 yes C 5 2 41
2 2019 7 yes C 7 1 31
2 2019 7 yes C 10 3 28
2 2020 8 no A 17 0 24
2 2020 8 no A 26 6 21
2 2020 8 no A 28 1 56
2 2020 8 no A 32 4 40
2 2020 8 no B 20 2 38
2 2020 8 no B 22 1 43
2 2020 8 no B 25 0 41
2 2020 8 no B 30 1 68
2 2020 8 no C 19 0 50
2 2020 8 no C 24 0 22
2 2020 8 no C 27 2 25
2 2020 8 no C 29 11 60
2 2020 8 yes A 2 9 34
2 2020 8 yes A 6 6 38
2 2020 8 yes A 12 8 39
2 2020 8 yes A 14 0 23
2 2020 8 yes B 5 0 36
2 2020 8 yes B 8 0 47
2 2020 8 yes B 11 0 54
2 2020 8 yes B 15 0 9
2 2020 8 yes C 3 1 20
2 2020 8 yes C 4 0 23
2 2020 8 yes C 10 1 46
2 2020 8 yes C 16 0 17
2 2018 9 no A 21 16 56
2 2018 9 no A 23 0 34
2 2018 9 no A 26 2 38
2 2018 9 no A 29 0 32
2 2018 9 no B 18 19 66
2 2018 9 no B 20 5 68
2 2018 9 no B 25 5 25
2 2018 9 no B 28 4 16
2 2018 9 no C 19 7 23
2 2018 9 no C 22 3 11
2 2018 9 no C 31 1 25
2 2018 9 no C 32 25 62
2 2018 9 yes A 2 0 38
2 2018 9 yes A 4 0 39
2 2018 9 yes A 6 0 56
2 2018 9 yes A 10 0 39
2 2018 9 yes B 7 13 51
2 2018 9 yes B 11 2 36
2 2018 9 yes B 15 1 52
2 2018 9 yes B 16 1 49
2 2018 9 yes C 3 2 18
2 2018 9 yes C 5 1 19
2 2018 9 yes C 9 3 41
2 2018 9 yes C 13 13 69
2 2019 10 no A 23 3 5
2 2019 10 no A 24 1 1
2 2019 10 no A 26 0 13
2 2019 10 no A 32 3 3
2 2019 10 no B 18 0 0
2 2019 10 no B 19 0 7
2 2019 10 no B 25 0 6
2 2019 10 no B 29 0 7
2 2019 10 no C 17 1 3
2 2019 10 no C 21 1 4
2 2019 10 no C 28 3 6
2 2019 10 no C 31 4 7
2 2019 10 yes A 6 0 9
2 2019 10 yes A 10 0 7
2 2019 10 yes A 13 5 4
2 2019 10 yes A 14 3 9
2 2019 10 yes B 1 2 4
2 2019 10 yes B 2 2 3
2 2019 10 yes B 4 5 21
2 2019 10 yes B 16 0 5
2 2019 10 yes C 3 0 4
2 2019 10 yes C 7 0 7
2 2019 10 yes C 9 0 0
2 2019 10 yes C 11 0 6
3 2019 1 no A 21 0 7
3 2019 1 no A 25 10 5
3 2019 1 no A 29 2 4
3 2019 1 no A 32 2 5
3 2019 1 no B 19 2 5
3 2019 1 no B 22 7 14
3 2019 1 no B 23 4 2
3 2019 1 no B 30 2 5
3 2019 1 no C 24 0 12
3 2019 1 no C 26 9 9
3 2019 1 no C 28 4 8
3 2019 1 no C 31 2 3
3 2019 1 yes A 5 3 4
3 2019 1 yes A 6 8 4
3 2019 1 yes A 7 0 3
3 2019 1 yes A 10 2 7
3 2019 1 yes B 3 3 5
3 2019 1 yes B 4 0 14
3 2019 1 yes B 12 1 10
3 2019 1 yes B 16 0 8
3 2019 1 yes C 2 2 4
3 2019 1 yes C 9 1 9
3 2019 1 yes C 14 1 14
3 2019 1 yes C 15 14 11
3 2021 2 no A 20 0 36
3 2021 2 no A 21 0 43
3 2021 2 no A 29 0 17
3 2021 2 no A 32 0 49
3 2021 2 no B 23 0 19
3 2021 2 no B 24 0 30
3 2021 2 no B 28 0 28
3 2021 2 no B 31 0 41
3 2021 2 no C 17 0 25
3 2021 2 no C 18 1 20
3 2021 2 no C 22 0 41
3 2021 2 no C 26 0 33
3 2021 2 yes A 10 0 25
3 2021 2 yes A 12 0 29
3 2021 2 yes A 13 0 14
3 2021 2 yes A 15 0 12
3 2021 2 yes B 3 1 32
3 2021 2 yes B 6 0 25
3 2021 2 yes B 9 0 47
3 2021 2 yes B 14 0 32
3 2021 2 yes C 2 0 49
3 2021 2 yes C 8 0 30
3 2021 2 yes C 11 0 20
3 2021 2 yes C 16 0 4
3 2019 3 no A 17 5 4
3 2019 3 no A 24 1 2
3 2019 3 no A 25 2 3
3 2019 3 no A 32 3 4
3 2019 3 no B 18 1 5
3 2019 3 no B 29 1 1
3 2019 3 no B 30 0 2
3 2019 3 no B 31 3 0
3 2019 3 no C 20 13 8
3 2019 3 no C 23 1 3
3 2019 3 no C 27 2 0
3 2019 3 no C 28 2 2
3 2019 3 yes A 4 3 10
3 2019 3 yes A 14 1 2
3 2019 3 yes A 15 0 0
3 2019 3 yes A 16 0 0
3 2019 3 yes B 5 2 3
3 2019 3 yes B 9 0 0
3 2019 3 yes B 12 0 1
3 2019 3 yes B 13 0 0
3 2019 3 yes C 1 13 3
3 2019 3 yes C 7 2 2
3 2019 3 yes C 8 0 0
3 2019 3 yes C 10 0 5
3 2019 4 no A 17 1 31
3 2019 4 no A 20 0 30
3 2019 4 no A 26 2 26
3 2019 4 no A 27 1 43
3 2019 4 no B 21 6 30
3 2019 4 no B 28 0 29
3 2019 4 no B 31 4 47
3 2019 4 no B 32 9 45
3 2019 4 no C 19 3 18
3 2019 4 no C 22 0 30
3 2019 4 no C 25 1 41
3 2019 4 no C 29 0 45
3 2019 4 yes A 3 4 53
3 2019 4 yes A 10 3 25
3 2019 4 yes A 13 3 53
3 2019 4 yes A 14 2 43
3 2019 4 yes B 6 0 35
3 2019 4 yes B 8 0 35
3 2019 4 yes B 12 1 40
3 2019 4 yes B 15 0 42
3 2019 4 yes C 1 0 19
3 2019 4 yes C 2 5 48
3 2019 4 yes C 5 0 54
3 2019 4 yes C 9 0 41
3 2020 5 no A 18 5 11
3 2020 5 no A 23 15 25
3 2020 5 no A 27 22 25
3 2020 5 no A 31 9 16
3 2020 5 no B 17 40 21
3 2020 5 no B 21 8 10
3 2020 5 no B 28 5 8
3 2020 5 no B 29 6 8
3 2020 5 no C 19 6 8
3 2020 5 no C 20 6 17
3 2020 5 no C 22 25 6
3 2020 5 no C 24 16 16
3 2020 5 yes A 1 0 11
3 2020 5 yes A 6 0 23
3 2020 5 yes A 10 3 6
3 2020 5 yes A 14 5 5
3 2020 5 yes B 3 18 14
3 2020 5 yes B 11 0 5
3 2020 5 yes B 12 7 5
3 2020 5 yes B 16 9 3
3 2020 5 yes C 2 4 7
3 2020 5 yes C 4 9 4
3 2020 5 yes C 7 5 8
3 2020 5 yes C 9 1 7
3 2021 6 no A 23 4 35
3 2021 6 no A 24 30 39
3 2021 6 no A 29 4 7
3 2021 6 no A 31 0 4
3 2021 6 no B 17 0 5
3 2021 6 no B 21 0 6
3 2021 6 no B 27 15 31
3 2021 6 no B 30 3 19
3 2021 6 no C 22 9 25
3 2021 6 no C 26 13 19
3 2021 6 no C 28 2 22
3 2021 6 no C 32 2 50
3 2021 6 yes A 9 1 13
3 2021 6 yes A 10 5 16
3 2021 6 yes A 15 1 16
3 2021 6 yes A 16 0 25
3 2021 6 yes B 4 0 9
3 2021 6 yes B 5 4 17
3 2021 6 yes B 7 1 15
3 2021 6 yes B 11 5 21
3 2021 6 yes C 1 1 26
3 2021 6 yes C 3 0 6
3 2021 6 yes C 8 0 21
3 2021 6 yes C 13 1 26
3 2020 7 no A 18 0 9
3 2020 7 no A 25 3 20
3 2020 7 no A 26 1 23
3 2020 7 no A 32 2 46
3 2020 7 no B 23 1 17
3 2020 7 no B 24 0 60
3 2020 7 no B 27 0 39
3 2020 7 no B 28 4 26
3 2020 7 no C 17 5 34
3 2020 7 no C 19 1 35
3 2020 7 no C 20 7 43
3 2020 7 no C 29 1 33
3 2020 7 yes A 12 0 24
3 2020 7 yes A 13 2 26
3 2020 7 yes A 15 0 43
3 2020 7 yes A 16 6 43
3 2020 7 yes B 1 1 44
3 2020 7 yes B 2 1 40
3 2020 7 yes B 3 0 47
3 2020 7 yes B 8 2 46
3 2020 7 yes C 4 1 60
3 2020 7 yes C 5 2 37
3 2020 7 yes C 7 2 27
3 2020 7 yes C 10 7 26
3 2021 8 no A 17 0 35
3 2021 8 no A 26 1 22
3 2021 8 no A 28 0 51
3 2021 8 no A 32 0 42
3 2021 8 no B 20 0 37
3 2021 8 no B 22 1 37
3 2021 8 no B 25 0 37
3 2021 8 no B 30 1 62
3 2021 8 no C 19 0 42
3 2021 8 no C 24 0 34
3 2021 8 no C 27 2 29
3 2021 8 no C 29 7 59
3 2021 8 yes A 2 4 35
3 2021 8 yes A 6 1 42
3 2021 8 yes A 12 9 39
3 2021 8 yes A 14 0 29
3 2021 8 yes B 5 0 38
3 2021 8 yes B 8 0 41
3 2021 8 yes B 11 0 48
3 2021 8 yes B 15 0 24
3 2021 8 yes C 3 0 36
3 2021 8 yes C 4 0 35
3 2021 8 yes C 10 1 52
3 2021 8 yes C 16 0 31
3 2019 9 no A 21 6 56
3 2019 9 no A 23 0 34
3 2019 9 no A 26 1 38
3 2019 9 no A 29 0 32
3 2019 9 no B 18 8 66
3 2019 9 no B 20 0 68
3 2019 9 no B 25 3 25
3 2019 9 no B 28 0 16
3 2019 9 no C 19 0 23
3 2019 9 no C 22 0 11
3 2019 9 no C 31 1 25
3 2019 9 no C 32 5 62
3 2019 9 yes A 2 0 38
3 2019 9 yes A 4 0 39
3 2019 9 yes A 6 0 56
3 2019 9 yes A 10 0 39
3 2019 9 yes B 7 0 51
3 2019 9 yes B 11 0 36
3 2019 9 yes B 15 0 52
3 2019 9 yes B 16 2 49
3 2019 9 yes C 3 1 18
3 2019 9 yes C 5 1 19
3 2019 9 yes C 9 0 41
3 2019 9 yes C 13 4 69
3 2020 10 no A 23 1 4
3 2020 10 no A 24 0 0
3 2020 10 no A 26 0 8
3 2020 10 no A 32 5 4
3 2020 10 no B 18 0 0
3 2020 10 no B 19 0 9
3 2020 10 no B 25 0 3
3 2020 10 no B 29 0 3
3 2020 10 no C 17 0 3
3 2020 10 no C 21 0 1
3 2020 10 no C 28 1 11
3 2020 10 no C 31 0 10
3 2020 10 yes A 6 0 11
3 2020 10 yes A 10 1 10
3 2020 10 yes A 13 2 5
3 2020 10 yes A 14 1 7
3 2020 10 yes B 1 0 5
3 2020 10 yes B 2 1 6
3 2020 10 yes B 4 0 30
3 2020 10 yes B 16 2 1
3 2020 10 yes C 3 1 3
3 2020 10 yes C 7 0 7
3 2020 10 yes C 9 0 0
3 2020 10 yes C 11 0 1
;
run;
proc glimmix method = laplace data = exper1;
class SITE Herb veg time plot;
model count = herb|veg|time / dist=negbin link=log;
random site year site*year*time / subject= site*herb*veg type=ar(1);
run; First, I coded my random statement accordingly for a staggered start design with random block and year effects, shown as “random site year site*year*time:, but this is for a design WITHOUT replication within block. But I’m having difficulty understanding how to incorporate random plot effects, as I can’t find guidance on the SAS rules for pooling effects when my design has replication within site as well as the other sources of variation. Second, I want to compare this Herb|Veg|Time model with a model that uses a covariate measured at the plot level in each year (Covar) to see if it is a better model than the previous model (comparison of model AICCs). I expect this covariate may be a good explanation for the year effect, and it has mechanistic importance that is more informative. I think the following SAS code is appropriate: model count=herb veg Covar*herb Covar*veg time time*herb time*veg; random site / subject=site*herb*veg type=ar(1); I am hopeful someone can assist with my questions about the SAS code for this design. Thank you for assistance and insights.
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