updateGamma: Function to update site level linear coefficients in the...

In BAREB: A Bayesian Repulsive Biclustering Model for Periodontal Data

Description

This function takes current parameters and observed data, gives an updated site level linear coefficients.

Usage

updateGamma(X, Y, Z, delta,
    Beta, Gamma, E, R,
    S, Ds, mu, mustar,
    sigma, c, step, runif,
    n, m, T0, p, q, D, theta,tau)

Arguments

X	the patient level covariate matrix
Y	the CAL observation matrix, with missing values
Z	the site level covariate matrix
delta	the missing indicator matrix, with 1 means missing
Beta	current patient level linear coefficients matrix
Gamma	current site level linear coefficients array
E	current patient level clustering vector
R	current site level clustering matrix
S	number of patient level clusters
Ds	a vector recording numbers of site level clusters
mu	current estimated mean matrix for CAL
mustar	current matrix of latent value for missingness model
sigma	current estimated noise variance
c	current c for missingness model. It is a vector
step	an array of steps for M-H
runif	an array of uniform random variables for deciding whether to accept new proposed point in M-H
n	number of patients
m	number of sites
T0	number of teeth
p	dimension of patient level covariates
q	dimension of site level covariates
D	the D matrix in the paper
theta	parameter for DPP
tau	parameter for DPP

Value

updateGamma(X, Y, Z, delta, Beta, Gamma, E, R, S, Ds, mu, mustar, sigma, c, step, runif, n, m, T0, p, q, D, theta,tau) returns a list with following variables:

Gamma	the updated site level linear coefficients
mu	the updated mu computed by updated Gamma
mustar	the updated mustar computed by updated Gamma

Author(s)

Yuliang Li

See Also

update_RJ for a complete example for all functions in this package.

BAREB documentation built on March 26, 2020, 7:36 p.m.