Interpretation of GLM output for binomial family

mbopi

New Member
#1
Hello everyone,

I need help interpreting GLM output for binomial family and determining if this GLM is the correct one to use according to what I need to analyze.

Noninvasive genetic sampling of a wild cat was conducted to determine sex. Different environmental factors were identified to determine any differences, similarities, or significant factors between both sexes: main habitat type(x1), edge type (x2), topography (x3), road type (x4), and survey region (x5).
The dependent variable (y1) was scat taken as binary data (male = 1, female = 0).

fit<-glm(y1~factor(x1)+factor(x2)+factor(x3)+factor(x4)+factor(x5),data=waterpots, family="binomial")

The following was obtained:

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.9589 2.0162 1.468 0.1422
factor(x1)CV -2.0651 1.0310 -2.003 0.0452 *
factor(x1)NF -5.6497 4326.6475 -0.001 0.9990
factor(x1)RM 15.5040 1751.7149 0.009 0.9929
factor(x1)SF -3.2241 1.3603 -2.370 0.0178 *
factor(x1)SF 33.0701 4326.6464 0.008 0.9939
factor(x2)B 0.2057 0.7263 0.283 0.7770
factor(x3)200m 0.4085 1.7143 0.238 0.8116
factor(x3)50m -1.7452 1.3750 -1.269 0.2044
factor(x4)FoR 1.1621 4326.6465 0.000 0.9998
factor(x5)Hokugan 0.8208 1.5572 0.527 0.5981
factor(x5)Komi -16.9234 1751.7156 -0.010 0.9923
factor(x5)Mihara -0.6663 1.4276 -0.467 0.6407
factor(x5)Otomi 1.9076 1.9800 0.963 0.3353

Then, stepwise regression using AIC was performed.

This is how I interpreted the data:
Negative results correspond to females since they equal to zero and all positive results correspond to males. Therefore, female cats are statistically significantly correlated to SF (secondary forest) and CV (coastal vegetation) (factor x1= main habitat type).
However, SF is the only variant that appears twice. And it always appears twice. I even modified factor (x1) data leaving only two scat sites along SF (both scat samples identified as males) and it still appears twice, albeit both are positive numerals.

I would kindly like to know how these results would be interpreted and if this is the correct GLM to use to analyze binary data with categorical explanatory variables.

Thank you!