Statistics: mnrfit

### Function Reference: `mnrfit`

statistics: B = mnrfit (X, Y)
statistics: B = mnrfit (X, Y, name, value)
statistics: [B, dev] = mnrrfit (…)
statistics: [B, dev, stats] = mnrfit (…)

Perform logistic regression for binomial responses or multiple ordinal responses.

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×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×P categorical or numerical matrix (containing only 1s and 0s) or an N×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.

Name |  | Value
--- | --- | ---
`"model"` |  | Specifies the type of model to fit. Currently, only `"ordinal"` is fully supported. `"nominal"` is only supported for 2 classes in Y.
`"display"` |  | A flag to enable/disable displaying information about the fitted model. Default is `"off"`.

`[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