.lambda_0_MH_cp {BayesFBHborrow}

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