files2coda {bayesSurv} | R Documentation |
This function creates a coda
mcmc
object from values found
in files where sampled values from bayessurvreg1
function are stored
or from data.frames.
files2coda(files, data.frames, variant = 1, dir = getwd(), start = 1, end, thin = 1, header = TRUE, chain)
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. |
A list with mcmc
objects. One object per file or data.frame.
Arnošt Komárek arnost.komarek[AT]mff.cuni.cz
## *** 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"))