I apologize if this question is very simple but I have found a lot of information on how to interpret log transformed variables (http://www.ats.ucla.edu/stat/mult_pkg/faq/general/log_transformed_regression.htm, Interpretation of log transformed predictor) but not on logit transformations and I am confused.
I have a linear mixed effect model in which the dependent variable is the proportion of home range overlap (logit transformed), and two predictor variables, home range size and season (untransformed):
logit(overlap proportion) ~ 1 + Home range size + season + 1|indiv_code
I want to know how to interpret the effect of the home range size on home range overlap. The beta coefficient of home range size is -0.0019829. My question then is:
Can I interpret the effect of home range size as I would do in any logistic regression?
And if so, could I then say that for every unit increase in home range size there will be a 99.80% increase in the odds of home range overlap, given that exp(-0.0019829) = 0.9980 (99.80%). This was my understanding after reading about how to interpret the effect of continuous variables in logistic regression (http://www.ats.ucla.edu/stat/mult_pkg/faq/general/odds_ratio.htm)