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I'm currently working on a logistic regression analysis in R where my response variable is 1 = used animal location and 0 = random location. I am modelling non-random habitat selection for a species of wildlife. I recently ran into an issue where I need to evaluate non-random habitat selection across 3 seasonal periods to determine if habitat selection varies by season. In other words, I need to determine if my continuous independent variables differ across season. Would it be appropriate to build a model with the continuous independent variables and include a seasonal categorical variable into the model? See example code below where R0A1 is defined as the used and random locations (1 and 0, respectively), MP_Scaled, MPHW_Scaled, HW_Scaled, YP_Scaled, AG_Scaled, and Shrub_Scaled are continuous variables, and Season is a categorical variable (1 = winter, 2 = preincubation, and 3 = summer). I recognize that logistic regression will treat one of the seasonal values as a reference category which is fine but need to determine if the continuous variables differ across season and if so, need to output the beta coefficients for each season.

results_full <- glmer(R0A1 ~ MP_Scaled+ MPHW_Scaled+ HW_Scaled +
                             YP_Scaled+ AG_Scaled+ Shrub_Scaled+
                             Season+ (1|ID)+ (1|Year)+ (1|Site),
                             data=secondorder, family=binomial)

summary(results_full)
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Using a dummy indicator variable might be more simple. This avoids constraining the seasonal effect due to a 1, 2 and three scoring.

If you have 3 seasons, then use 2 variables; say winter and preincubation. For a summer observation both these variables are 0. For a winter observation, winter = 1 and preincubation = 0. For a preincubation observation winter = 0 and preincubation = 1.

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  • $\begingroup$ You're welcome. If you think the answer is usable, you can check the check mark on the left of the answer to indicate the validity of the answer! $\endgroup$ – spdrnl May 12 '15 at 14:11

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