I am exploring the probability of flight in a seabird (1=flight, 0=no flight) using binomial logistic regression. My predictors are distance to a disturbance (continuous), hour of the day (continuous), site (factor), season (factor), sea state (dichotomous), and group size (dichotomous). I have explored the use of piecewise regression in relation to the distance to a disturbance as this variable spans a large range (out to 74 km) and there is no way that this is affecting flight at the largest distance.
When the model was fit with just reference to distance to a disturbance within the R program 'segmented' it points to a break in the data at 3.9 km. The slope up to this distance is negative and statistically significant while the slope estimate for distances further than 3.9 km is estimated to be 0 and non-significant.
I would like to now sequentially add in additional terms to the model to see if there is any reduction in the deviance when the additional terms are added. Can a term be added just to the section before or after the break? I cannot seem to find any information in the literature regarding this
My questions is can I do this? Or do I need to split the data into two chunks, before and after the breakpoint and explore additional terms this way.
Also the motivation to do this analysis is more to find and identify the breakpoint. Instead of adding in terms after I assess the breakpoint should I explore the breakpoint within a the model including all the terms? Would this find the break in the data in relation to the other terms or does the algorithm completely ignore the other terms in the model when searching for a break in the distance to disturbance variable.