updateE | R Documentation |
This function takes current parameters and observed data, gives an updated patient level clustering.
updateE( Beta, Gamma,w,
X, Y, Z, delta,
E, R, S, Ds,
mu, mustar,
sigma, c,
n, m, T0, p, q, D)
Beta |
current patient level linear coefficients matrix |
Gamma |
current site level linear coefficients array |
w |
current patient level clustering prior prob, a vector |
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 |
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 |
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 |
updateE( Beta, Gamma,w, X, Y, Z, delta,E, R, S, Ds, mu, mustar, sigma, c, n, m, T0, p, q, D)
returns a list with following variables:
E |
the updated patient level clustering |
Ds |
new vector recording the numbers of site level clusters |
mu |
the updated mu computed by updated E |
mustar |
the updated mustar computed by updated E |
Yuliang Li
update_RJ for a complete example for all functions in this package.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.