[R] Modelling categorical variables

Jessica Lavabre
Tue, 08 Sep 2015 09:01:14 -0000

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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