I'm trying to figure out if I can use the ordinal logistic regression function with the data I have in order to create a set of prediction equations for estimating age-at-death. My response variables are ordinal (adolescent, young adult, middle adult, old adult), and my four predictor variables are continuous, but two are non-linear. I've tried transforming the predictor variables, but I can't seem to find a linear solution. If I've read correctly, the ordinal logistic regression analysis in JMP is based on a general linear model, so would that negate my being able to use the function due to my non-linear response variables (determined by the means of these variables when graphed by age category - i.e., the mean values rise to an asymptote in middle adulthood, then fall in value in old adulthood)? I've tried performing the analysis using the non-linear platform, but I receive an cautionary message because my Y variable is not continuous.
*Any* constructive advice is much appreciated! Also, on the most basic level, am I understanding the assumptions of the logistic model correctly?
The assumptions for the independent variables isn't linearity ( and I don't even know what that would mean?)
A generalized linear models means the model is a linear combination, but the terms themselves can be quadratic. A non-linear model may have order statistics (max/min) and/or division/multiplication of terms that can't be expressed in a linear fashion.