bayesDensity {bayesSurv} | R Documentation |

## Summary for the density estimate based on the mixture Bayesian AFT model.

### Description

Function to summarize the results obtained using
`bayessurvreg1`

function.

Compute the conditional (given the number of mixture components) and unconditional estimate of the density function based on the values sampled using the reversible jumps MCMC (MCMC average evaluated in a grid of values).

Give also the values of each sampled density evaluated at that grid (returned as the attribute of the resulting object). Methods for printing and plotting are also provided.

### Usage

```
bayesDensity(dir, stgrid, centgrid, grid, n.grid = 100,
skip = 0, by = 1, last.iter,
standard = TRUE, center = TRUE, unstandard = TRUE)
```

### Arguments

`dir` |
directory where to search for files ‘mixmoment.sim’, ‘mweight.sim’, mmean.sim', ‘mvariance.sim’ with the McMC sample. |

`stgrid` |
grid of values at which the sampled standardized
densities are to be evaluated. If |

`centgrid` |
grid of values at which the sampled centered (but not
scaled) densities are to be evaluated. If |

`grid` |
grid of values at which the sampled densities are to be
evaluated. If |

`n.grid` |
the length of the grid if |

`skip` |
number of rows that should be skipped at the beginning of each *.sim file with the stored sample. |

`by` |
additional thinning of the sample. |

`last.iter` |
index of the last row from *.sim files that should be
used. If not specified than it is set to the maximum available
determined according to the file |

`standard` |
if |

`center` |
if |

`unstandard` |
of |

### Value

An object of class `bayesDensity`

is returned. This object is a
list and has potentially three components: `standard`

,
`center`

and
`unstandard`

. Each of these three components is a `data.frame`

with as many rows as number of grid points at which the density was
evaluated and with columns called ‘grid’, ‘unconditional’ and ‘k = 1’,
..., ‘k = k.max’ giving a predictive errr density, either averaged
over all sampled `k`

s (unconditional) or averaged over a
psecific number of mixture components.

Additionally, the object of class `bayesDensity`

has three
attributes:

`sample.size` |
a vector of length |

`moments` |
a |

`k` |
a |

There exist methods to print and plot objects of the class `bayesDensity`

.

### Author(s)

Arnošt Komárek arnost.komarek@mff.cuni.cz

### References

Komárek, A. (2006).
*Accelerated Failure Time Models for Multivariate
Interval-Censored Data with Flexible Distributional Assumptions*.
PhD. Thesis, Katholieke Universiteit Leuven, Faculteit Wetenschappen.

Komárek, A. and Lesaffre, E. (2007).
Bayesian accelerated failure time model for correlated interval-censored data
with a normal mixture as an error distribution.
*Statistica Sinica*, **17**, 549–569.

### Examples

```
## See the description of R commands for
## the models described in
## Komarek (2006),
## Komarek and Lesaffre (2007),
##
## R commands available
## in the documentation
## directory of this package
## - ex-cgd.R and
## https://www2.karlin.mff.cuni.cz/~komarek/software/bayesSurv/ex-cgd.pdf
##
## - ex-tandmobMixture.R and
## https://www2.karlin.mff.cuni.cz/~komarek/software/bayesSurv/ex-tandmobMixture.pdf
##
```

*bayesSurv*version 3.7 Index]