updateGamma | R Documentation |
This function takes current parameters and observed data, gives an updated site level linear coefficients.
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)
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 |
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 |
Yuliang Li
update_RJ for a complete example for all functions in this package.
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