bayesGspline {bayesSurv} | R Documentation

## Summary for the density estimate based on the model with Bayesian G-splines.

### Description

Compute the estimate of the density function based on the values sampled using the MCMC (MCMC average evaluated in a grid of values) in a model where density is specified as a Bayesian G-spline.

This function serves to summarize the MCMC chains related to the distributional parts of the considered models obtained using the functions: `bayesHistogram`, `bayesBisurvreg`, `bayessurvreg2`, `bayessurvreg3`.

If asked, this function returns also the values of the G-spline evaluated in a grid at each iteration of MCMC.

### Usage

    bayesGspline(dir = getwd(), extens="", extens.adjust="_b",
       grid1, grid2, skip = 0, by = 1, last.iter, nwrite,
       only.aver = TRUE, standard = FALSE, version = 0)

### Arguments

`dir` | directory where to search for files (`mixmoment.sim', `mweight.sim', `mmean.sim', `gspline.sim') with the MCMC sample.
`extens` | an extension used to distinguish different sampled G-splines if more G-splines were used in one simulation (e.g. with doubly-censored data or in the model where both the error term and the random intercept were defined as the G-splines). According to which `bayes*survreg*` function was used, specify the argument `extens` in the following way. bayesHistogram: always `extens = ""` bayesBisurvreg: quad to compute the bivariate distribution of the error term for the onset time: `extens = ""`; to compute the bivariate distribution of the error term for the event time if there was doubly-censoring: `extens = "_2"`; bayessurvreg2: quad to compute the distribution of the error term for the onset time: `extens = ""`; to compute the distribution of the error term for the event time if there was doubly-censoring: `extens = "_2"`; bayessurvreg3: quad to compute the distribution of the error term for the onset time: `extens = ""`; to compute the distribution of the error term for the event time if there was doubly-censoring: `extens = "_2"`; to compute the distribution of the random intercept for the onset time: `extens = "_b"`; to compute the distribution of the random intercept term for the event time if there was doubly-censoring: `extens = "_b2"`;
- `extens.adjust` | this argument is applicable for the situation when the MCMC chains were created using the function `bayessurvreg3`, i.e. when both the distribution of the error term and the random intercept was specified as the G-spline. In that case the location of the error term and the random intercept are separately not identifiable. Only the location of the sum epsilon + b can be estimated. For this reason, the function `bayesGspline` always centers the distribuytion of the random intercept to have a zero mean and adds its original mean to the mean of the distribution of the error term. Argument `extens.adjust` is used to match correctly the files containing the G-spline of the random intercept corresponding to the particular error term. The following values of `extens.adjust` should be used in the following situations: if there are no doubly-censored data or if we are computing the distribution of the error term/random intercept from the model for the onset time then
  `extens.adjust = "_b"`
- if there are doubly-censored data and we are computing the distribution of the error term/random intercept from the model for the event time then
  `extens.adjust = "_b2"`
`grid1` | grid of values from the first dimension at which the sampled densities are to be evaluated.
`grid2` | grid of values from the second dimension (if the G-spline was bivariate) at which the sampled densities are to be evaluated. This item is `missing` if the G-spline is univariate.
`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 `mixmoment.sim`.
`nwrite` | frequency with which is the user informed about the progress of computation (every `nwrite`th iteration count of iterations change).
`only.aver` | `TRUE/FALSE`, if `TRUE` only MCMC average is returned otherwise also values of the G-spline at each iteration are returned (which might ask for quite lots of memory).
`standard` | `TRUE/FALSE`, if `TRUE`, each G-spline is standardized to have zero mean and unit variance. Only applicable if `version` = 30 or 31, otherwise `standard` is always set to `FALSE`.
`version` | this argument indicates by which `bayes*survreg*` function the chains used by `bayesGspline` were created. Use the following: bayesHistogram: `version = 0`; bayesBisurvreg: `version = 0`; bayessurvreg2: `version = 0`; bayessurvreg3: `version = 30` or `31`. Use `version = 30` if you want to compute the density of the error term; `version = 31` if you want to compute the density of the random intercept.

### Value

An object of class `bayesGspline` is returned. This object is a list with components `grid`, `average` for the univariate G-spline and components `grid1`, `grid2`, `average` for the bivariate G-spline.

`grid` | this is a grid of values (vector) at which the McMC average of the G-spline was computed.
`average` | these are McMC averages of the G-spline (vector) evaluated in `grid`.
`grid1` | this is a grid of values (vector) for the first dimension at which the McMC average of the G-spline was computed.
`grid2` | this is a grid of values (vector) for the second dimension at which the McMC average of the G-spline was computed.
`average` | this is a matrix `length(grid1)` times `length(grid2)` with McMC averages of the G-spline evaluated in
x1 = | ( grid1 | ... | grid1 )
and
| ( | grid2 | )
x2 = | ( | ... | )
| ( | grid2 | )

There exists a method to plot objects of the class `bayesGspline`.

### Attributes

Additionally, the object of class `bayesGspline` has the following attributes:

`sample.size`
  a length of the McMC sample used to compute the McMC average.
`sample`
  G-spline evaluated in a grid of values. This attribute is present only if `only.aver = FALSE`.

  For a univariate G-spline this is a matrix with `sample.size` columns and length(grid1) rows.

  For a bivariate G-spline this is a matrix with `sample.size` columns and length(grid1)*length(grid2) rows.

### Author(s)

Arnost Komarek arnost.komarek@med.kuleuven.be

### References

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

Komarek, A. and Lesaffre, E. (2006). Bayesian accelerated failure time model with multivariate doubly-interval-censored data and flexible distributional assumptions. Submitted.
See `Komarek_Lesaffre_2006.pdf`.

Komarek, A. and Lesaffre, E. (2006b). Bayesian semiparametric accelerated failurew time model for paired doubly-interval-censored data. Submitted.
See `Komarek_Lesaffre_2006b.pdf`.

Komarek, A. Lesaffre, E., and Legrand, C. (2006). EORTC data article. To be written.

### Examples

    ## See the description of R commands for
    ## the models described in
    ## Komarek (2006),
    ## Komarek and Lesaffre (2006),
    ## Komarek and Lesaffre (2006b),
    ## Komarek, Lesaffre, and Legrand (2006).
    ##
    ## R commands available (or soon available)
    ## in the documentation
    ## directory of this package
    ## as tandmobCS.pdf, tandmobCS.R
    ##    tandmobPA.pdf, tandmobPA.R.
    ##

[Package bayesSurv version 0.4 Index]