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)?