Stepwiseglm with non-binary response variable (classifications into 3 buckets) and mix of continous/categorical predictor variables

Hi there,

I have an array of 25 predictors that looks like this (not, converted from values like "Albania" to numbers like 46 for each country, for example, for categorical variables, and some with continuous variables):

0  5  841  93  10679  1101  1  161  50  3  3  1  1
0  0  0  0  0  0  14  161  50  13  9  1  1
0  0  0  0  0  0  3  161  83  13  3  1  1
0  0  5  0  17  41  22  161  83  3  2  1  1
0  0  0  0  0  0  4  161  83  3  3  1  1
0  0  0  0  0  0  8  161  71  3  3  1  1
6  4  12  3  0  0  4  161  83  3  3  1  2
0  7  1  0  0  0  13  161  71  3  3  1  2
0  1  262  23  961  96  102  55  198  5  3  1  1
0  5  285  23  968  96  12  161  83  3  3  1  1
0  0  843  95  10681  1101  18  71  116  13  3  1  1
0  0  0  1  0  0  1  104  78  5  3  2  1

and an array of 1 response variables that looks like (note, converted from text to being ordinal variable):

1
2
3
2
1
3

I ran

stepwiseglm(predictormatrix,response);

And got the below output:

1. Adding x16, Deviance = 1614.387, FStat = 1743.1408, PValue = 0
2. Adding x25, Deviance = 1440.549, FStat = 2142.3401, PValue = 0
3. Adding x19, Deviance = 1363.947, FStat = 996.9882, PValue = 6.0490784e-213
4. Adding x2, Deviance = 1318.3645, FStat = 613.74033, PValue = 3.1244367e-133
5. Adding x6, Deviance = 1302.0096, FStat = 222.96267, PValue = 4.115593e-50
6. Adding x4, Deviance = 1286.8418, FStat = 209.20522, PValue = 3.7970611e-47
7. Adding x13, Deviance = 1270.84, FStat = 223.47373, PValue = 3.1943538e-50
8. Adding x7, Deviance = 1260.2216, FStat = 149.5337, PValue = 3.0111563e-34
9. Adding x21, Deviance = 1252.7396, FStat = 105.98862, PValue = 8.7078938e-25
10. Adding x4:x21, Deviance = 1244.1865, FStat = 121.98614, PValue = 2.8737974e-28
11. Adding x16:x21, Deviance = 1237.2232, FStat = 99.866125, PValue = 1.8808007e-23
12. Adding x2:x4, Deviance = 1231.7667, FStat = 78.598656, PValue = 8.3183569e-19
13. Adding x3, Deviance = 1225.9525, FStat = 84.143346, PValue = 5.094845e-20
14. Adding x3:x21, Deviance = 1221.1573, FStat = 69.665335, PValue = 7.5377414e-17
15. Adding x6:x13, Deviance = 1217.5627, FStat = 52.372501, PValue = 4.7786344e-13
16. Adding x2:x7, Deviance = 1215.1946, FStat = 34.569465, PValue = 4.1868738e-09
17. Adding x19:x21, Deviance = 1213.154, FStat = 29.835612, PValue = 4.7659952e-08
....

How should I interpret this? Is this telling me that I should choose the combo that was output with the lowest p value, and highest F stat? Am I missing certain modelspec and name value pair arguments within this link that would make much more sense given my data?

Thanks,