.lambda_0_MH_cp {BayesFBHborrow} | R Documentation |
Lambda_0 MH step, proposal from conditional conjugate posterior
Description
Lambda_0 MH step, proposal from conditional conjugate posterior
Usage
.lambda_0_MH_cp(
df_hist,
Y_0,
I_0,
X_0 = NULL,
s,
beta_0 = NULL,
mu,
sigma2,
lambda,
lambda_0,
tau,
bp_0 = 0,
J,
clam,
a_lam = 0.01,
b_lam = 0.01,
lambda_0_count = 0,
lambda_0_move = 0
)
Arguments
df_hist |
data.frame from dataframe_fun() |
Y_0 |
historical trial data |
I_0 |
historical trial censoring indicator |
X_0 |
historical trial design matrix |
s |
split point locations, (J+2) |
beta_0 |
parameter value for historical covariates |
mu |
prior mean for baseline hazard |
sigma2 |
prior variance hyperparameter for baseline hazard |
lambda |
baseline hazard |
lambda_0 |
historical baseline hazard |
tau |
borrowing parameter |
bp_0 |
number of covariates, length(beta_0) |
J |
number of split points |
clam |
controls neighbor interactions, in range (0, 1) |
a_lam |
lambda hyperparameter, default is 0.01 |
b_lam |
lambda hyperparameter, default is 0.01 |
lambda_0_count |
number of total moves for lambda_0 |
lambda_0_move |
number of accepted moves for lambda_0 |
Value
list of updated (if accepted) lambda_0 and data.frames, as well as the number of accepted moves
[Package BayesFBHborrow version 2.0.1 Index]