# Read the sampled values from the Bayesian survival regression model to a coda mcmc object. This function creates a `coda` `mcmc` object from values found in files where sampled values from `bayessurvreg1` function are stored or from data.frames. ```r 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. ## Returns A list with `mcmc` objects. One object per file or data.frame. ## Author(s) Arnošt Komárek arnost.komarek@mff.cuni.cz ## Examples ```r ## *** 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")) ```

files2coda function