I have a large amount of vegetation data that has been broken down into 13 habitat classes. I am trying to determine which vegetation tends to fall into or is absent from which habitat with any sort of significance. I have been put onto running a multinomial logistic regression, specifically using glmnet (as I have approximately 200 variables, and only about 260 observations).
Running cv.glmnet using the code:
I get a list of numbers that I am struggling to understand, however I found the code:
Which returns the coefficients for each variable for each habitat class for the lambda that is 1 SE larger than the minimum Lambda value (which as far as I can tell the generally accepted lambda value).
(Intercept) 0.7914263664 Salix 0.0000000000 Mash 0.0000000000 Pin 0.0000000000 Choke . Betula 0.0025260258 Ideae 0.0000000000 Leather 0.0000000000
What I'm wondering, using these coefficients, is it possible to state that those values with the largest magnitude (either closest to -1 and +1) are the most important in defining that class, which those close to 0 are unimportant, and those with periods were removed during the cv.glmnet. So in this case the plant "Betula" would be more influential than all others, and "Choke" was so uninfluential that it was removed? Also, no idea what intercept means, but I imagine I can find that one on my own.