11-21-2023
bowerske
Calcite | Level 5
Member since
04-03-2023
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Latest posts by bowerske
Subject Views Posted 2372 11-21-2023 06:37 PM 2486 11-17-2023 06:24 PM 2488 11-17-2023 06:20 PM 2593 11-16-2023 06:10 PM 948 04-05-2023 03:34 PM 1021 04-04-2023 04:03 PM -
Activity Feed for bowerske
- Posted Re: fixed effect estimate and lsmeans are zero on Statistical Procedures. 11-21-2023 06:37 PM
- Posted Re: fixed effect estimate and lsmeans are zero on Statistical Procedures. 11-17-2023 06:24 PM
- Posted Re: fixed effect estimate and lsmeans are zero on Statistical Procedures. 11-17-2023 06:20 PM
- Liked Re: fixed effect estimate and lsmeans are zero for SteveDenham. 11-17-2023 03:31 PM
- Posted fixed effect estimate and lsmeans are zero on Statistical Procedures. 11-16-2023 06:10 PM
- Posted Re: Improving model fit in proc glimmix, interpretation of chi-sq/df? on Statistical Procedures. 04-05-2023 03:34 PM
- Posted Improving model fit in proc glimmix, interpretation of chi-sq/df? on Statistical Procedures. 04-04-2023 04:03 PM
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Posts I Liked
Subject Likes Author Latest Post 1
11-21-2023
06:37 PM
Hi Steve, I added the ilink to the model statement and now the model doesn't converge : ) Kristen
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11-17-2023
06:24 PM
Yes, thank you. That was the problem. The logs that were inoculated in 2016 were only harvested once, so there weren't two levels of harvest for the 2016 inoculation. I created a Cohort variable to capture this information but now I am getting a negative estimate that shouldn't be negative (Tallow B, which were inoculated in 2015 and harvested in 2016).
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11-17-2023
06:20 PM
Yes, that was the problem- We had inoculated logs in 2015 that we harvested in 2015 and those same logs in 2016 and then we inoculated new logs in 2016 that we only harvested in 2016 (so there isn't a combination of HarvestYear=2016 *InoculationYear=1 and that's were the zeros were coming from). My solution was to create three cohorts (A=inoculated and harvested in 2015, B= inoculated in 2015 and harvested in 2016, and C=inoculated and harvested in 2016). However, now I am getting a negative lsmeans estimate for Tallow B, which doesn't make any sense.
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11-16-2023
06:10 PM
We did an experiment where we grew mushrooms on two species of tree logs across three different farms. The mushrooms were harvested and weighed with logs inoculated the first year harvested twice and logs inoculated the second year harvested once. Code and output below. Why is fixed effect estimate zero and it cannot estimate lsmeans for oak and tallow, even thought it does for oak and tallow by inoculation and harvest year? ods graphics on; proc sort data= mushroom.data; by TreeSp innocyear harvestyear; proc glimmix data=mushroom.data2; class Farm TreeSp TreeID innocyear harvestyear; model Weight_Cum= TreeSp innocyear harvestyear innocyear*harvestyear*TreeSp /ddfm=kr2 solution; random intercept /subject =Farm; random TreeID(TreeSp) /group=farm; lsmeans TreeSp innocyear*harvestyear*TreeSp/ilink lines /*pdiff=all CL*/; run; quit;
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04-05-2023
03:34 PM
Hi Steve, I will try that; I didn't start off with that because even though I did seed 10 weevils in each pot, we didn't always recover 10. Does that matter? Kristen
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04-04-2023
04:03 PM
Hi, I am analyzing data from an experiment testing whether insects from northern latitudes are more cold-hardy that those from southern latitudes. I have five large cages in each of three locations (north, mid, south) Each cage has 21 potted plants on which I placed 10 insects from north, mid, or southern populations( 21= 3 sources of insects x 7 months). Each month I remove one potted plant per source from each cage and count what proportion of insects survived (The variable prop is a calculated variable equal to live insects/found insects). The code that I think is the best so far is below. I have also tried binomial, which had worse fit statistics and neg binomial, which didn’t converge. proc glimmix data=puncv.new plots=pearsonpanel (conditional marginal); class Month Location Source Cage; model prop= Month Location Source ; random intercept /subject=Cage; run; When I run this, I get the below fit statistics and residual graphs? What is the generalized chi-sq/df= 0.05 mean about how the model fits? Is there a model that would produce better residual graphs (i.e. fit the data better?)
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