dot-lambda_MH_cp: Lambda MH step, proposal from conditional conjugate posterior

In BayesFBHborrow: Bayesian Dynamic Borrowing with Flexible Baseline Hazard Function

Lambda MH step, proposal from conditional conjugate posterior

Description

Lambda MH step, proposal from conditional conjugate posterior

Usage

.lambda_MH_cp(
  df_hist,
  df_curr,
  Y,
  I,
  X,
  s,
  beta,
  beta_0 = NULL,
  mu,
  sigma2,
  lambda,
  lambda_0,
  tau,
  bp,
  bp_0 = 0,
  J,
  a_lam = 0.01,
  b_lam = 0.01,
  lambda_move = 0,
  lambda_count = 0,
  alpha = 0.3
)

Arguments

df_hist	data.frame from dataframe_fun()
df_curr	data.frame from dataframe_fun()
Y	data
I	censoring indicator
X	design matrix
s	split point locations, J + 2
beta	parameter value for covariates
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	number of covariates, length(beta)
bp_0	number of covariates, length(beta_0)
J	number of split points
a_lam	lambda hyperparameter
b_lam	lambda hyperparameter
lambda_move	number of accepted lambda moves
lambda_count	total number of lambda moves
alpha	power parameter

Value

list of updated (if accepted) lambda and data.frames, as well as the number of accepted moves

BayesFBHborrow documentation built on Sept. 30, 2024, 9:17 a.m.