files2coda {bayesSurv} | R Documentation

## Read the sampled values from the Bayesian survival regression model to a coda mcmc object.

### Description

This function creates a `coda` `mcmc` object from values found in files where sampled values from `bayessurvreg1` function are stored or from data.frames.

### Usage

    files2coda(files, data.frames, variant = 1, dir = getwd(),
       start = 1, end, thin = 1, header = TRUE, chain)

### Arguments

`files` | a vector of strings giving the names of files that are to be converted to `coda` objects. If missing and `data.frames` is also missing, all appropriate files found in a directory `dir` are converted to coda objects. File "iteration.sim" is always used (if found) to index the sampled values. If this file is not found the sampled values are indexed from 1 to the sample size. If "mixture.sim" appeares here, only the column with number of mixture components is converted to the coda object.
`data.frames` | a vector of strings giving the names of data.frames that are to be converted to `coda` objects.
`variant` | a variant of `bayessurvreg` function used to generate sampled values. This argument is only used to identify appropriate files when `files` argument is missing. Currently only 1 is supported to cooperate with `bayessurvreg1`.
`dir` | string giving the directory where it will be searched for the files with sampled values.
`start` | the first row (possible header does not count) from the files with the sampled values that will be converted to coda objects.
`end` | the last row from the files with the sampled values that will be converted to coda objects. If missing, it is the last row in files.
`thin` | additional thinning of sampled values (i.e. only every `thin` value from `files` and `data.frames` is considered).
`header` | TRUE or FALSE indicating whether the files with the sampled values contain also the header on the first line or not.
`chain` | parameter giving the number of the chain if parallel chains were created and sampled values stored in data.frames further stored in lists(). If `missing`, data.frames are not assumed to be stored in lists.

### Value

A list with `mcmc` objects. One object per file or data.frame.

### Author(s)

Arnost Komarek arnost.komarek@med.kuleuven.be

### Examples

    ## *** illustration of usage of parameters 'data.frames' and 'chain' ***
    ## *********************************************************************
    ## Two parallel chains with four variables, stored in data.frames
    ## data.frames are further stored in lists
    library(coda)

    group1 <- list();  group2 <- list();  group3 <- list()
        ## first chain of first two variables:
      group1[[1]] <- data.frame(var1 = rnorm(100, 0, 1), var2 = rnorm(100, 5, 4))
        ## second chain of first two variables:
      group1[[2]] <- data.frame(var1 = rnorm(100, 0, 1), var2 = rnorm(100, 5, 4))
        ## first chain of the third variable:
      group2[[1]] <- data.frame(var3 = rgamma(100, 1, 1))
        ## second chain of the third variable:
      group2[[2]] <- data.frame(var3 = rgamma(100, 1, 1))
        ## first chain of the fourth variable:
      group3[[1]] <- data.frame(var4 = rbinom(100, 1, 0.4))
        ## second chain of the fourth variable:
      group3[[2]] <- data.frame(var4 = rbinom(100, 1, 0.4))

      ## Create mcmc objects for each chain separately
    mc.chain1 <- files2coda(data.frames = c("group1", "group2", "group3"), chain = 1)
    mc.chain2 <- files2coda(data.frames = c("group1", "group2", "group3"), chain = 2)

      ## Create mcmc.list to represent two parallel chains
    mc <- mcmc.list(mc.chain1, mc.chain2)
    rm(mc.chain1, mc.chain2)

    ## *** illustration of usage of parameter 'data.frames' without 'chain' ***
    ## ************************************************************************
    ## Only one chain for four variables was sampled and stored in three data.frames
        ## chain of first two variables:
      group1 <- data.frame(var1 = rnorm(100, 0, 1), var2 = rnorm(100, 5, 4))
        ## chain of the third variable:
      group2 <- data.frame(var3 = rgamma(100, 1, 1))
        ## chain of the fourth variable:
      group3 <- data.frame(var4 = rbinom(100, 1, 0.4))

      ## Create an mcmc object
    mc <- files2coda(data.frames = c("group1", "group2", "group3"))

[Package bayesSurv version 0.4 Index]