# Modelling categorical variable

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 is as follow:

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       129.59          127.344        131.383
N17    N      B         62.829       72.827          76.736          77.644
N22    N      B         89.207       78.719          75.005          81.176
N33    N      A         23.288       35.48            25.317          36.931
N40    N      B         36.734       62.234          30.68            61.885
N47    N      E         60.443       66.367          150.892        55.097


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)?

Since module appears to be a categorical variable with multiple levels, one usual model would be multinomial logistic regression. In 'R' this is available with the multinom function in the nnet package.