Store the parameters of a fitted multinomial logistic regression model. The model is used to predict probabilities of \(K\) classes.




A 3D array of stacked matrices. The number of matrices (i.e., the number of slices in the cube) should be equal to \(K-1\). Each matrix is contains samples of the regression coefficients under sampling uncertainty corresponding to a particular class. Rows index parameter samples and columns index coefficients.


An object of class params_mlogit, which is a list containing coefs and n_samples, where n_samples is equal to the number of rows in each element of coefs.


Multinomial logit models are used to predict the probability of membership for subject \(i\) in each of \(K\) classes as a function of covariates: $$Pr(y_i = c) = \frac{e^{\beta_c x_i}}{\sum_{k=1}^K e^{\beta_k x_i}}$$


params <- params_mlogit(coefs = array( c(matrix(c(intercept = 0, treatment = log(.75)), nrow = 1), matrix(c(intercept = 0, treatment = log(.8)), nrow = 1)), dim = c(1, 2, 2) ))