pi_compute function

Compute Probability of Each True Outcome, for Every Subject

pi_compute(beta, X, n, n_cat)

Arguments

• beta: A numeric column matrix of regression parameters for the Y (true outcome) ~ X (predictor matrix of interest).
• X: A numeric design matrix.
• n: An integer value specifying the number of observations in the sample. This value should be equal to the number of rows of the design matrix, X.
• n_cat: The number of categorical values that the true outcome, Y, can take.

Returns

pi_compute returns a matrix of probabilities, $P(Y_i = j | X_i) = \frac{\exp(X_i \beta)}{1 + \exp(X_i \beta)}$

for each of the $i = 1, \dots,$ n subjects. Rows of the matrix correspond to each subject. Columns of the matrix correspond to the true outcome categories $j = 1, \dots,$ n_cat.