Let's assume one has looked at the occurence of a plant species at different altitude, different temperature and environment. Here are its data:
Temperature environment altitude plant1 18.1 mud 812 plant2 15.3 field 754 plant3 17.4 mud 213 plant4 15.2 forest 678 plant5 16.6 field 1023 etc...
This guy wants to know if the abondance of plants (number of plants) that one can find is dependent on the three variables. He could run a GLM with poisson distributed errors for the
environment variable with these data:
Number of plants environment 311 forest 102 mud 71 field etc...
How could he do in order to evaluate the effect of continuous variables on the abondance of plants without having to cut the variable into chunks (
Temperature: [10.1:13], [13.1:16], [16.1:19])? What type of model can incorporates continuous and categorial variables in order to makes sense of count data?
Would it make sense to run two Kolmogorov-Smirnov tests on
altitude to check if these variables are uniformly distributed and a test GLM, poisson distributed errors for
environment? But then what happen if it tends to have more
mud in high altitude? He would need one model with all variables.