Posterior prediction for model checking — SLCA

Predict numbers of recurrent events.

Usage

SLCA_predict(alpha, beta, d, Z, mu_censor, gamma = 0)

alpha: estimated alpha - multinomial regression coefficients for latent class membership
beta: estimated beta - class
d: a vector of observed recurrent events for subjects of interest
Z: a vector of time-independent corvariates
mu_censor: a vector of estimated mu(C), where C is a vector of censoring time
gamma: individual frailty. 0 represents the frailty equals 1 and k reprsents the frailty follows gamma(k,k)

A list containing the following components:

`PosteriorPredict`	A vector of posterior prediction for observed events for subjects of interest

`tauhat`	A matrix of posterior probability of latent class membership