Hello Suzana.


> I've been trying to apply GLM for spatial analysis of  a marine animal's
> distribution (as the dependant variable) related to many environmental
> variables (as the independant ones). But some of them are correlated, for
> example, the depth increases as the distance to coast increases, and  I
> must test both. Does it disagree with the assumption of independence of the
> data? If yes, how can I fix it in order to keep the two variables in the
> model?
>
>
Generally, data in the predictor set are always correlated; the issue is
how much correlation we have to tolerate. If this correlation is so strong,
most healthy is delete some of those variables because the collinearity
problem will be present. If this correlation is weak, possibly the effect
on the model will be negligible. Anyway, the best way to address this
situation is measuring the collinearity with appropiate indexes such as
variance inflation factor.


> I have other doubt: is it possible to use uncountable variables (e.g.
> categorical) as independant variables with other countable ones in the same
> formula? If yes, how should I do that?
>
>
This is not clear for me. Uncontable such as colours or types of rocks? If
so, they could be included in the model in the same way as another variable:

glm(y~x+uncountable_variable,family=...




> Thank you so much.
>
>
HTH




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