We are trying to find some tutorials on using and running Generalized Linear Mixed Models (GLMM) in SPSS software. We are working with seaweeds, and ran an experiment for 4 weeks, testing two different seeding densities. We have 3 replicates for each density and would like to know the effect of different seeding densities and time on the area of coverage and length of juveniles. After some research, we found that a GLMM would be our best option but we have some difficulty finding resources on how to do it using SPSS and reporting the results. Some guidance would be appreciated
SPSS is not the best way to fit a GLMM model. consider the lme4 package in R.
assuming:
Response Variables:
Area of coverage
Length of juveniles
Predictor Variables:
Seeding density
Time (assuming it's a categorical variable)
Interaction term between seeding density and time (if applicable)
Random Effects:
Replicates nested within seeding density
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here is the code to do it:
glmm_model
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