Can Matlab perform generalized ordinal logistic regression?

I have a dataset of 3 independent variables and 1 dependent variable (the predictor, a vector of x values). As I have 3 independent variables I am trying to classify, I wish to use the mnrfit function. However, I believe that my data does not satisfy the proportional odds assumption so I wish to use a generalized ordinal logit model (non-proportional odds model) or a partial proportional odds model such as the gologit2 function in STATA, documentation shown in the following link.

https://www.stata.com/meeting/4nasug/gologit2.pdf

Description of the partial and non-proportial odds model I am thinking about

http://www.iijsr.org/data/frontImages/gallery/Vol._1_No._3/2._8-23.pdf

Documentation of the mnrfit function states:

" You can alternatively fit a model with different intercept and slopes among the categories by using the 'interactions','on' name-value pair argument. However, using this option for ordinal models when the equal slopes model is true causes a loss of efficiency (you lose the advantage of estimating fewer parameters). "

I interpret this as saying changing the 'interactions' 'on' pair value makes the mnrfit function behave like the STATA function where it does that require the proportional odds assumption.

Given this, will mnrfit output values for me to calculate the % chance of getting either of my three distributions at any given x?

[Jialing Lin]

Hi,

I have also encountered the same question. Have you found a solution?

Thanks!