Is it possible to set up multinomial logistic regression with multiple ordinal predictors?

I am trying to generate a regression model that takes in 9 ordinal inputs(X) and returns 1 ordinal output (Y).

If I treat my X matrix of predictors like they are continuous (all ranging in integer values from 0 to 3, some only from 0 to 2), the model has relatively large coefficients (e.g. -1.68e15), or it just errors out with the following output:

Index exceeds matrix dimensions.

Error in mnrfit>ordinalFit (line 380)
        pi = [gam(:,1) diffgam 1-gam(:,k-1)];

Error in mnrfit (line 206)
            ordinalFit(x,z,m,pi,flink,ilink,dlink,n,k,p,pstar,parallel);

Error in GroomingOntomFLS_multiNomOrdRegress (line 51)
    val = mnrfit( dataMatrix, responseVec, 'model', 'ordinal');

Each column of my `dataMatrix` variable is a separate, ordinal predictor. Is it possible to define them explicitly as ordinal variables?

Answer 1

[Corey Silva]

This definitely should be possible but hard without seeing your inputs. I think the following example should help you out: https://www.mathworks.com/help/stats/mnrfit.html#btpyj65

[Benjamin]

Unfortunately, this works only with an ordinal response, not with ordinal predictors.