Updated help files for functions with names starting from g to m.

BayesianTools/R/MAP.R

@@ -1,20 +1,20 @@
-#' calculates the Maxiumum APosteriori value (MAP)
-#' @author Florian Hartig
-#' @param bayesianOutput an object of class BayesianOutput (mcmcSampler, smcSampler, or mcmcList)
-#' @param ... optional values to be passed on the the getSample function 
-#' @details Currently, this function simply returns the parameter combination with the highest posterior in the chain. A more refined option would be to take the MCMC sample and do additional calculations, e.g. use an optimizer, a kerne delnsity estimator, or some other tool to search / interpolate around the best value in the chain
-#' @seealso \code{\link{WAIC}}, \code{\link{DIC}}, \code{\link{marginalLikelihood}}
-#' @export
-MAP <- function(bayesianOutput, ...){
-
- samples = getSample(bayesianOutput, parametersOnly = F, ...)
-
- if("mcmcSamplerList" %in% class(bayesianOutput)) nPars <- bayesianOutput[[1]]$setup$numPars
- else nPars = bayesianOutput$setup$numPars
-
- best = which.max(samples[,nPars + 1])
-
- return(list(parametersMAP = samples[best, 1:nPars], valuesMAP = samples[best, (nPars + 1):(nPars + 3)] ))
-
-}
+#' calculates the Maxiumum APosteriori value (MAP)
+#' @author Florian Hartig
+#' @param bayesianOutput an object of class BayesianOutput (mcmcSampler, smcSampler, or mcmcList)
+#' @param ... optional values to be passed on the the getSample function 
+#' @details Currently, this function simply returns the parameter combination with the highest posterior in the chain. A more refined option would be to take the MCMC sample and do additional calculations, e.g. use an optimizer, a kernel density estimator, or some other tool to search / interpolate around the best value in the chain.
+#' @seealso \code{\link{WAIC}}, \code{\link{DIC}}, \code{\link{marginalLikelihood}}
+#' @export
+MAP <- function(bayesianOutput, ...){
+ 
+ samples = getSample(bayesianOutput, parametersOnly = F, ...)
+ 
+ if("mcmcSamplerList" %in% class(bayesianOutput)) nPars <- bayesianOutput[[1]]$setup$numPars
+ else nPars = bayesianOutput$setup$numPars
+ 
+ best = which.max(samples[,nPars + 1])
+ 
+ return(list(parametersMAP = samples[best, 1:nPars], valuesMAP = samples[best, (nPars + 1):(nPars + 3)] ))
+
+}
 

BayesianTools/R/getVolume.R

@@ -1,35 +1,35 @@
-#' Calculate posterior volume
-#' @author Florian Hartig
-#' @param sampler an object of superclass bayesianOutput or any other class that has the getSample function implemented (e.g. Matrix)
-#' @param prior schould also prior volume be calculated
-#' @param method method for volume estimation. Currently, the only option is "MVN"
-#' @param ... additional parameters to pass on to the \code{\link{getSample}}
-#' @details The idea of this function is to provide an estimate of the "posterior volume", i.e. how "broad" the posterior is. One potential application is to the overall reduction of parametric uncertainty between different data types, or between prior and posterior. 
-#' 
-#' Implemented methods for volume estimation:
-#' 
-#' Option "MVN" - in this option, the volume is calculated as the determinant of the covariance matrix of the prior / posterior sample. 
-#' 
-#' @example /inst/examples/getVolume.R
-#' @export
-getVolume <- function(sampler, prior = F, method = "MVN", ...){
-
- x = getSample(sampler, ...)
-
- if(method == "MVN"){
- nPars = ncol(x)
- postVol = det(cov(x))
- }else stop("BayesianTools: unknown method argument in getVolume")
-
- if(prior == T){
- x = sampler$setup$prior$sampler(5000)
-
- if(method == "MVN"){
- nPars = ncol(x)
- priorVol = det(cov(x))
- }else stop("BayesianTools: unknown method argument in getVolume")
- return(list(priorVol = priorVol, postVol = postVol))
- }else return(postVol)
-}
-
-
+#' Calculate posterior volume
+#' @author Florian Hartig
+#' @param sampler an object of superclass bayesianOutput or any other class that has implemented the getSample function (e.g. Matrix)
+#' @param prior logical, should prior volume be calculated?
+#' @param method method for volume estimation. Currently, the only option is "MVN"
+#' @param ... additional parameters to pass on to the \code{\link{getSample}}
+#' @details The idea of this function is to provide an estimate of the "posterior volume", i.e. how "broad" the posterior is. One potential application is the overall reduction of parametric uncertainty between different data types, or between prior and posterior. 
+#' 
+#' Implemented methods for volume estimation:
+#' 
+#' Option "MVN" - in this option, the volume is calculated as the determinant of the covariance matrix of the prior / posterior sample. 
+#' 
+#' @example /inst/examples/getVolume.R
+#' @export
+getVolume <- function(sampler, prior = F, method = "MVN", ...){
+ 
+ x = getSample(sampler, ...)
+ 
+ if(method == "MVN"){
+ nPars = ncol(x)
+ postVol = det(cov(x))
+ }else stop("BayesianTools: unknown method argument in getVolume")
+ 
+ if(prior == T){
+ x = sampler$setup$prior$sampler(5000)
+ 
+ if(method == "MVN"){
+ nPars = ncol(x)
+ priorVol = det(cov(x))
+ }else stop("BayesianTools: unknown method argument in getVolume")
+ return(list(priorVol = priorVol, postVol = postVol))
+ }else return(postVol)
+}
+ 
+