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I am a beginner with statistics and R and have no clue on how to model my data. I have collected information on seed traps (ID) that includes the habitat type (Hab) and different measures of distances. Also I have applied a modularity analysis, so that the seeds traps are grouped into modules. My dataset looks like this:

 ID Hab Module DistEdge MeanDist1 MeanDist2 MeanDist3
F48   F      A   21.768    24.941     6.033    27.642
F50   F      E   35.666    60.505   149.927    48.582
F52   F      B   12.243   103.041    72.908   102.375
N02   N      B   58.681   129.590   127.344   131.383
N17   N      B   62.829    72.827    76.736    77.644

I am looking for a way to analyze if there is any correlation between the Module classification and the other variables. My difficulties here are:

1 - is there a way to model my data where Module is the response variable (something like Module~Hab*DistEdge*MeanDist1) ? If so, which model should I use (I only have a bit of experience with glm) and which distribution?

2 - Is that a problem if I have different types of predictor variable (factor and numerical)?

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  • $\begingroup$ This is basically a classification problem. Have a look at the rpart package. $\endgroup$
    – jlhoward
    Commented Sep 9, 2015 at 8:54

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There are several methods, perhaps the most common is multinomial logistic regression. In R you can use the multinom function in the nnet package:

library(nnet)
m1 <- multinom(Module~Hab+DistEdge + MeanDist1 + MeanDist2 + MeanDist3)

Other possibilities include trees (with rpart or party or another package), multinomial probit (quite similar to multinomial logistic, usually), discriminant analysis (but it makes a lot of assumptions).

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