postPii {skipTrack}

Compute M-H draw for pi_i in Li algorithm

Description

This performs a Metropolis-Hastings draw for pi_i, assuming s_ij follows a truncated geometric distribution with parameters pi_i and S. The proposal distribution for pi_i is Beta(alpha, beta).

Usage

postPii(sij, currentPii, priorA, priorB, S)

Arguments

sij

Vector of cycle skip indicators for individual i

currentPii

Current value of pi_i

priorA

Hyperparameter alpha.

priorB

Hyperparameter beta.

S

Maximum number of skips allowed in algorithm

Value

Draw for pi_i, repeated for the number of observations from individual i