updateBeta | R Documentation |
This function takes current parameters and observed data, gives an updated patient level linear coefficients.
updateBeta(X, Y, Z,
delta, Beta, Gamma, E, R,
S, Ds, mustar, mu,
sigma, c, 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 |
mustar |
current matrix of latent value for missingness model |
mu |
current estimated mean matrix for CAL |
sigma |
current estimated noise variance |
c |
current c for missingness model. It is a vector |
C |
current kernel matrix for DPP |
step |
a matrix of steps for M-H |
runif |
a matrix 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 |
updateBeta(X, Y, Z, delta, Beta, Gamma, E, R, S, Ds, mustar, mu, sigma, c, C, step, runif, n, m, T0, p, q, D, theta,tau)
returns a list with following variables:
C |
the updated kernel matrix computed by updated Beta |
Beta |
the updated patient level linear coefficients |
mu |
the updated mu computed by updated Beta |
mustar |
the updated mustar computed by updated Beta |
Yuliang Li
update_RJ for a complete example for all functions in this package.
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