.lambda_MH_cp {BayesFBHborrow}

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