Updated some functions starting with b and c.

BayesianTools/R/blockUpdate.R

@@ -1,8 +1,7 @@
-
 #' Determine the groups of correlated parameters
 #' @author Stefan Paul
 #' @param chain MCMC chain including only the parameters (not logP,ll, logP)
-#' @param blockSettings list with settings
+#' @param blockSettings a list with settings
 #' @return groups
 #' @keywords internal
 updateGroups <- function(chain,blockSettings){
@@ -60,7 +59,7 @@ getBlock <- function(blockSettings){
 
 
 #' getblockSettings
-#' @description Transforms the original settings in settings used in the model runs
+#' @description Transforms the original settings to settings used in the model runs
 #' @param blockUpdate input settings
 #' @return list with block settings
 #' @keywords internal
@@ -88,4 +87,5 @@ getBlockSettings <- function(blockUpdate){
  return(list(blockUpdateType = blockUpdateType, h = h, k = k, pSel = pSel,
  pGroup = pGroup, groups = groups))
  }
-
+
+

BayesianTools/R/codaFunctions.R

@@ -1,11 +1,8 @@
 #' Function to combine chains
-#' 
 #' @param x a list of MCMC chains
-#' @param merge logical determines whether chains should be merged
+#' @param merge logical, should chains be merged? (T or F)
 #' @return combined chains
-#' 
 #' @note to combine several chains to a single McmcSamplerList, see \code{\link{createMcmcSamplerList}}
-#' 
 #' @keywords internal
 combineChains <- function(x, merge = T){
 
@@ -40,14 +37,13 @@ combineChains <- function(x, merge = T){
 }
 
 
-
 #' Helper function to change an object to a coda mcmc class,
 #'
 #' @param chain mcmc Chain
-#' @param start for mcmc samplers start value in the chain. For SMC samplers, start particle
-#' @param end for mcmc samplers end value in the chain. For SMC samplers, end particle
+#' @param start for MCMC samplers, the initial value in the chain. For SMC samplers, start particle
+#' @param end for MCMC samplers, the end value in the chain. For SMC samplers, end particle.
 #' @param thin thinning parameter
-#' @return object of class coda::mcmc
+#' @return object an object of class coda::mcmc
 #' @details Very similar to coda::mcmc but with less overhead
 #' @keywords internal
 makeObjectClassCodaMCMC <- function (chain, start = 1, end = numeric(0), thin = 1){

BayesianTools/R/convertCoda.R

@@ -1,16 +1,11 @@
-
 #' Convert coda::mcmc objects to BayesianTools::mcmcSampler
-#' @description Function is used to make the plot and diagnostic functions 
-#' available for coda::mcmc objects
-#' @param sampler An object of class mcmc or mcmc.list 
-#' @param names vector giving the parameter names (optional)
-#' @param info matrix (or list with matrices for mcmc.list objects) with three coloumns containing log posterior, log likelihood and log prior of the sampler for each time step (optional; but see Details)
-#' @param likelihood likelihood function used in the sampling (see Details)
-#' @details The parameter 'likelihood' is optional for most functions but can be needed e.g for 
-#' using the \code{\link{DIC}} function.
-#' 
-#' Also the parameter info is optional for most uses. However for some functions (e.g. \code{\link{MAP}})
-#' the matrix or single coloumns (e.g. log posterior) are necessary for the diagnostics.
+#' @description Function to support plotting and diagnostic functions for coda::mcmc objects.
+#' @param sampler an object of class mcmc or mcmc.list 
+#' @param names a vector with parameter names (optional)
+#' @param info a matrix (or list with matrices for mcmc.list objects) with three columns containing log posterior, log likelihood and log prior of the sampler for each time step (optional; but see Details)
+#' @param likelihood likelihood function used for sampling (see Details)
+#' @details The parameter 'likelihood' is optional for most functions but can be needed e.g for \code{\link{DIC}} function.
+#' Also, the parameter information is typically optional for most uses. However, for certain functions (e.g. \code{\link{MAP}}), the matrix or single columns (e.g. log posterior) are necessary for diagnostics.
 #' @export
 
 convertCoda <- function(sampler, names = NULL, info = NULL, likelihood = NULL){