Note: This function is currently a wrapper for the logistic_regression function. It can only be used for fitting an ordinal logistic model and a nominal model with 2 categories (which is an ordinal case). Hierarchical models as well as nominal model with more than two classes are not currently supported. This function is a work in progress.

B = mnrfit (X, Y) returns a matrix, B, of coefficient estimates for a multinomial logistic regression of the nominal responses in Y on the predictors in X. X is an $N\times P$ numeric matrix the observations on predictor variables, where $N$ corresponds to the number of observations and $P$ corresponds to predictor variables. Y contains the response category labels and it either be an $N\times P$ categorical or numerical matrix (containing only 1s and 0s) or an $N\times 1$ numeric vector with positive integer values, a cell array of character vectors and a logical vector. Y can also be defined as a character matrix with each row corresponding to an observation of X.

B = mnrfit (X, Y, name, value) returns a matrix, B, of coefficient estimates for a multinomial model fit with additional parameterss specified Name-Value pair arguments.

[B, dev, stats] = mnrfit (… also returns the deviance of the fit, dev, and the structure stats for any of the previous input arguments. stats currently only returns values for the fields "beta", same as B, "coeffcorr", the estimated correlation matrix for B, "covd", the estimated covariance matrix for B, and "se", the standard errors of the coefficient estimates B.

See also: logistic_regression

Source Code: mnrfit