GibbsMH.NoBorrow {BayesFBHborrow} | R Documentation |

## GibbsMH sampler, without Bayesian Borrowing

### Description

An MCMC sampler for time-to-event data, without Bayesian Borrowing. We obtain a flexible baseline hazard function by making the split points random within a piecewise exponential model and using a Gaussian Markov random field prior to smooth the baseline hazards. Only calls the sampler and does not run any input checks. Best practice is to call BayesFBHborrow(), if the user is not familiar with the model at hand.

### Usage

```
## S3 method for class 'NoBorrow'
GibbsMH(
Y,
I,
X = NULL,
Y_0 = NULL,
I_0 = NULL,
X_0 = NULL,
tuning_parameters,
hyperparameters = list(a_sigma = 1, b_sigma = 1, phi = 3, clam_smooth = 0.8),
lambda_hyperparameters = list(a_lambda = 0.01, b_lambda = 0.01),
iter = 1500L,
warmup_iter = 10L,
refresh = 0,
max_grid = 2000L
)
```

### Arguments

`Y` |
data |

`I` |
event indicator |

`X` |
design matrix |

`Y_0` |
historical data, default is NULL |

`I_0` |
historical event indicator, default is NULL |

`X_0` |
historical design matrix, default is NULL |

`tuning_parameters` |
list of "cprop_beta", "Jmax", and "pi_b" |

`hyperparameters` |
list containing the hyperparameters c("a_sigma", "b_sigma", "clam_smooth", "phi"). |

`lambda_hyperparameters` |
contains two hyperparameters (a_lambda and b_lambda) used for the update of lambda, default is c(0.01, 0.01) |

`iter` |
number of iterations for MCMC sampler, excluding warmup, default is 2000 |

`warmup_iter` |
number of warmup iterations (burn-in) for MCMC sampler, default is 2000 |

`refresh` |
number of iterations between printed screen updates, default is 500 |

`max_grid` |
grid size for the smoothed baseline hazard, default is 2000 |

### Value

list with values after each iteration for parameters: out_fixed (J, mu, sigma2, beta), lambda, s, as well as tuning values of the total number of accepts: lambda_move and beta_move. Also included is the out_slam which contains the shrunk estimate of the baseline hazard.

### Examples

```
set.seed(123)
# Load example data and set your hyper parameters
data(weibull_cc, package = "BayesFBHborrow")
data(weibull_hist, package = "BayesFBHborrow")
# The datasets consists of 3 (2) columns named "tte", "event" and "X".
# To explicitly run the sampler, extract the samples as following
Y <- weibull_cc$tte
I <- weibull_cc$event
X <- matrix(weibull_cc$X_trt)
# Specify hyperparameters and tuning parameters
hyper <- list("a_sigma" = 2,
"b_sigma" = 2,
"clam_smooth" = 0.5,
"phi" = 3)
tuning_parameters <- list("Jmax" = 5,
"pi_b" = 0.5,
"cprop_beta" = 0.5)
# Set initial values to 'NULL' for default settings
output <- GibbsMH(Y, I, X, NULL, NULL, NULL,
tuning_parameters = tuning_parameters, hyperparameters = hyper,
iter = 5, warmup_iter = 1)
```

*BayesFBHborrow*version 2.0.1 Index]