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Multinomial logistic regression values
pihat = mnrval(B,X) returns the predicted probabilities for the multinomial logistic regression model with predictors, X, and the coefficient estimates, B.
pihat is an n-by-k matrix of predicted probabilities for each multinomial category. B is the vector or matrix that contains the coefficient estimates returned by mnrfit. And X is an n-by-p matrix which contains n observations for p predictors.
[pihat,dlow,dhi] = mnrval(B,X,stats) also returns 95% error bounds on the predicted probabilities, pihat, using the statistics in the structure, stats, returned by mnrfit.
The lower and upper confidence bounds for pihat are pihat minus dlow and pihat plus dhi, respectively. Confidence bounds are nonsimultaneous and only apply to the fitted curve, not to new observations.
[pihat,dlow,dhi] = mnrval(B,X,stats,Name,Value) returns the predicted probabilities and 95% error bounds on the predicted probabilities pihat, with additional options specified by one or more Name,Value pair arguments.
For example, you can specify the model type, link function, and the type of probabilities to return.
[yhat,dlow,dhi] = mnrval(B,X,ssize,stats) also computes 95% error bounds on the predicted counts yhat, using the statistics in the structure, stats, returned by mnrfit.
The lower and upper confidence bounds for yhat are yhat minus dlo and yhat plus dhi, respectively. Confidence bounds are nonsimultaneous and they apply to the fitted curve, not to new observations.
[yhat,dlow,dhi] = mnrval(B,X,ssize,stats,Name,Value) returns the predicted category counts and 95% error bounds on the predicted counts yhat, with additional options specified by one or more Name,Value pair arguments.
For example, you can specify the model type, link function, and the type of predicted counts to return.