Hi,
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 *
* 81.176N33 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)?
Any help would be greatly appreciated,
-- Jessica Lavabre-Micas
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