Update s3 class - fixed problems in R CMD check

BayesianTools/DESCRIPTION

@@ -40,7 +40,7 @@ Suggests:
     testthat
 LinkingTo: Rcpp
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 URL: https://github.com/florianhartig/BayesianTools
 BugReports: https://github.com/florianhartig/BayesianTools/issues
 VignetteBuilder: knitr

BayesianTools/NAMESPACE

@@ -27,7 +27,6 @@ S3method(print,smcSamplerList)
 S3method(summary,mcmcSampler)
 S3method(summary,mcmcSamplerList)
 S3method(summary,smcSampler)
-S3method(summary,smcSamplerList)
 export(DE)
 export(DEzs)
 export(DIC)

BayesianTools/R/classBayesianOutput.R

@@ -1,21 +1,27 @@
 # NOTE: The functions in this class are just templates that are to be implemented for all subclasses of BayesianOutput. They are not functional. 
 
 
-#' Extracts the sample from a bayesianOutput
-#' @author Florian Hartig
-#' @param sampler an object of class mcmcSampler, mcmcSamplerList, smcSampler, smcSamplerList, mcmc, mcmc.list, double, numeric
+#' Extracts samples from a bayesianOutput
+#' @description
+#' This function extracts samples from a BT MCMC or SMC sampler after they have been run. It can also work on objects, for example coda::mcmc, matrix, 
+#' 
+#' It can return either a matrix or a coda mcmc object. 
+#' 
+#' @param sampler an object, typically of class mcmcSampler, mcmcSamplerList (created by \code{\link{runMCMC}}), smcSampler, smcSamplerList, mcmc, mcmc.list, double, numeric
 #' @param parametersOnly for a BT output, if F, likelihood, posterior and prior values are also provided in the output
 #' @param coda works only for mcmc classes - provides output as a coda object. Note: if mcmcSamplerList contains mcmc samplers such as DE that have several chains, the internal chains will be collapsed. This may not be the desired behavior for all applications.
 #' @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 thin thinning parameter. Either an integer determining the thinning intervall (default is 1) or "auto" for automatic thinning.
+#' @param thin thinning parameter. Either an integer determining the thinning interval (default is 1) or "auto" for automatic thinning.
 #' @param numSamples sample size (only used if thin = 1). If you want to use numSamples set thin to 1.
 #' @param whichParameters possibility to select parameters by index
 #' @param reportDiagnostics logical, determines whether settings should be included in the output
 #' @param ... further arguments
 #' @example /inst/examples/getSampleHelp.R
 #' @details If thin is greater than the total number of samples in the sampler object the first and the last element (of each chain if a sampler with multiples chains is used) are sampled. If numSamples is greater than the total number of samples all samples are selected. In both cases a warning is displayed.
 #' @details If thin and numSamples is passed, the function will use the thin argument if it is valid and greater than 1, else numSamples will be used.
+#' @return a matrix or a coda object, depending on option
+#' @author Florian Hartig
 #' @export
 getSample <- function(sampler, parametersOnly = T, coda = F, start = 1, end = NULL, thin = 1, numSamples = NULL, whichParameters = NULL, reportDiagnostics = FALSE, ...) UseMethod("getSample")
 
@@ -219,6 +225,9 @@ getSample.MCMC <- function(sampler, parametersOnly = T, coda = F, start = 1, end
   return(getSample(as.matrix(sampler$mvSamples), parametersOnly = parametersOnly, coda = coda, start = start, end = end, thin = thin, numSamples = numSamples, whichParameters = whichParameters, reportDiagnostics = reportDiagnostics))
 }
 
+
+# getSample implementation for objects created by Bridge Sampling???
+
 #' @rdname getSample
 #' @author Tankred Ott
 #' @export

BayesianTools/R/classBayesianSetup.R

@@ -1,8 +1,8 @@
 
 #' Creates a standardized collection of prior, likelihood and posterior functions, including error checks etc.
 #' @author Florian Hartig, Tankred Ott
-#' @param likelihood log likelihood density function
-#' @param prior either a prior class (see \code{\link{createPrior}}) or a log prior density function
+#' @param likelihood either an object of class likelihood created by \code{\link{createLikelihood}} or a specialized likelihood function, or a log likelihood density function. If you provide a function, parallel, paralleOptions and catchDuplicates will be internally used to create the likelihood object
+#' @param prior either an object of a prior created by \code{\link{createPrior}} or a log prior density function
 #' @param priorSampler if a prior density (and not a prior class) is provided to prior, the optional prior sampling function can be provided here
 #' @param lower vector with lower prior limits
 #' @param upper vector with upper prior limits
@@ -14,6 +14,9 @@
 #' @param plotLower vector with lower limits for plotting
 #' @param plotUpper vector with upper limits for plotting
 #' @param plotBest vector with best values for plotting
+#' 
+#' @returns An object of class BayesianSetup. Implemented S3 generics include \code{\link{print.BayesianSetup}}.
+#' 
 #' @details If prior is of class prior (e.g. create with \code{\link{createPrior}}), priorSampler, lower, upper and best will be ignored.\cr If prior is a function (log prior density), priorSampler (custom sampler), or lower/upper (uniform sampler) is required.\cr If prior is NULL, and lower and upper are passed, a uniform prior (see \code{\link{createUniformPrior}}) will be created with boundaries lower and upper.
 #' 
 #' For parallelization, Bayesiantools requies that the likelihood can evaluate several parameter vectors (supplied as a matrix) in parallel. 
@@ -28,6 +31,7 @@
 #' @seealso \code{\link{checkBayesianSetup}} \cr
 #'          \code{\link{createLikelihood}} \cr
 #'          \code{\link{createPrior}} \cr
+#'        
 #' @example /inst/examples/classBayesianSetup.R
 #' 
 #' 
@@ -185,9 +189,19 @@ stopParallel <- function(bayesianSetup){
 }
 
 
+#' Print an object of BayesianSetup
+#'
+#' @description print function for objects of class BayesianSetup
+#'
+#' 
+#' @param x an object of class BayesianSetup 
+#' @param ... not implemented
+#' 
 #' @author Maximilian Pichler
+#' 
 #' @export
-
+#' @seealso \code{\link{checkBayesianSetup}} \cr
+#'          \code{\link{createBayesianSetup}} \cr
 print.BayesianSetup <- function(x, ...){
   cat('BayesianSetup: \n\n')
 

BayesianTools/R/classLikelihood.R

@@ -1,10 +1,11 @@
-#' Creates a standardized likelihood class#'
+#' Creates a standardized likelihood class
+#' 
 #' @author Florian Hartig
-#' @param likelihood Log likelihood density
-#' @param names Parameter names (optional)
+#' @param likelihood log likelihood density
+#' @param names parameter names (optional)
 #' @param parallel parallelization , either i) no parallelization --> F, ii) native R parallelization --> T / "auto" will select n-1 of your available cores, or provide a number for how many cores to use, or iii) external parallelization --> "external". External means that the likelihood is already able to execute parallel runs in form of a matrix with 
-#' @param catchDuplicates Logical, determines whether unique parameter combinations should only be evaluated once. Only used when the likelihood accepts a matrix with parameter as columns. 
-#' @param parallelOptions list containing two lists. First "packages" determines the R packages necessary to run the likelihood function. Second "objects" the objects in the global envirnment needed to run the likelihood function (for details see \code{\link{createBayesianSetup}}).
+#' @param catchDuplicates logical, determines whether unique parameter combinations should only be evaluated once. Only used when the likelihood accepts a matrix with parameter as columns. 
+#' @param parallelOptions list containing two lists. First "packages" determines the R packages necessary to run the likelihood function. Second "objects" the objects in the global environment needed to run the likelihood function (for details see \code{\link{createBayesianSetup}}).
 #' @param sampler sampler
 #' @seealso \code{\link{likelihoodIidNormal}} \cr
 #'          \code{\link{likelihoodAR1}} \cr

BayesianTools/R/classMcmcSampler.R

@@ -1,5 +1,5 @@
-# Functions for class mcmcSamper
-
+# Functions for class mcmcSampler
+#' @rdname getSample
 #' @author Florian Hartig
 #' @export
 getSample.mcmcSampler <- function(sampler, parametersOnly = T, coda = F, start = 1, end = NULL, thin = 1, numSamples = NULL, whichParameters = NULL, reportDiagnostics= F, ...){
@@ -113,10 +113,18 @@ getSample.mcmcSampler <- function(sampler, parametersOnly = T, coda = F, start =
 
 
 
+#' Summmary of MCMC output
+#' @description
+#' Creates a summary table of a MCMC output
+#' @param object object of class mcmcSampler or mcmcSamplerList
+#' @param printCorrelation if set to TRUE, prints correlation among samples
+#' @param ... not implemented  
 #' @method summary mcmcSampler
 #' @author Stefan Paul
 #' @export
+#' @seealso \code{\link{getSample.mcmcSampler}}
 summary.mcmcSampler <- function(object, printCorrelation = "auto", ...){
+
   #codaChain = getSample(sampler, parametersOnly = parametersOnly, coda = T, ...)
   #summary(codaChain)
   #rejectionRate(sampler$codaChain)
@@ -209,9 +217,14 @@ summary.mcmcSampler <- function(object, printCorrelation = "auto", ...){
   }
 }
 
-#' @author Florian Hartig
+#' Prints MCMC output
+#' @description
+#' Prints MCMC output
+#' @param x object of class mcmcSampler or mcmcSamplerList
+#' @param ... additional options 
 #' @method print mcmcSampler
 #' @export
+#' @seealso \code{\link{getSample.mcmcSampler}}
 print.mcmcSampler <- function(x, ...){
   print("mcmcSampler - you can use the following methods to summarize, plot or reduce this class:")
   print(methods(class ="mcmcSampler"))
@@ -220,9 +233,17 @@ print.mcmcSampler <- function(x, ...){
   #effectiveSize(sampler$codaChain)
 }
 
-#' @author Florian Hartig
+
+
+#' Plots of MCMC output
+#' @description
+#' Plots MCMC output
+#' @param x object of class mcmcSampler or mcmcSamplerList
+#' @param ... additional options passed to tracePlot
 #' @method plot mcmcSampler
+#' @author Florian Hartig
 #' @export
+#' @seealso \code{\link{getSample.mcmcSampler}}
 plot.mcmcSampler <- function(x, ...){
   tracePlot(x, ...)
 }

BayesianTools/R/classMcmcSamplerList.R

@@ -1,8 +1,9 @@
 #' Convenience function to create an object of class mcmcSamplerList from a list of mcmc samplers
 #' @author Florian Hartig
-#' @param mcmcList a list with each object being an mcmcSampler
+#' @param mcmcList a list of objects of class mcmcSampler 
 #' @return Object of class "mcmcSamplerList"
 #' @export
+#' 
 createMcmcSamplerList <- function(mcmcList){
   # mcmcList <- list(mcmcList) -> This line didn't make any sense at all. Better would be to allow the user to simply provide several inputs without a list, but I guess the list option should be maintained, as this is convenient when scripting.
   for (i in 1:length(mcmcList)){
@@ -14,6 +15,7 @@ createMcmcSamplerList <- function(mcmcList){
 
 #' @author Stefan Paul
 #' @method summary mcmcSamplerList
+#' @describeIn summary.mcmcSampler 
 #' @export
 summary.mcmcSamplerList <- function(object, ...){
   #codaChain = getSample(sampler, parametersOnly = parametersOnly, coda = T, ...)
@@ -100,8 +102,10 @@ summary.mcmcSamplerList <- function(object, ...){
 
 }
 
+
 #' @author Florian Hartig
 #' @method print mcmcSamplerList
+#' @describeIn print.mcmcSampler 
 #' @export
 print.mcmcSamplerList <- function(x, ...){
   print("mcmcSamplerList - you can use the following methods to summarize, plot or reduce this class:")
@@ -112,13 +116,15 @@ print.mcmcSamplerList <- function(x, ...){
 }
 
 #' @method plot mcmcSamplerList
+#' @describeIn plot.mcmcSampler 
 #' @export
 plot.mcmcSamplerList <- function(x, ...){
   tracePlot(x, ...)
 }
 
 #' @author Florian Hartig
 #' @export
+#' @rdname getSample
 getSample.mcmcSamplerList <- function(sampler, parametersOnly = T, coda = F, start = 1, end = NULL, thin = 1, numSamples = NULL, whichParameters = NULL, reportDiagnostics, ...){
 
   if(!is.null(numSamples)) numSamples = ceiling(numSamples/length(sampler))

BayesianTools/R/classPosterior.R

@@ -1,7 +1,7 @@
 #' Creates a standardized posterior class
 #' @author Florian Hartig
 #' @param prior prior class
-#' @param likelihood Log likelihood density
+#' @param likelihood log likelihood density
 #' @details Function is internally used in \code{\link{createBayesianSetup}} to create a standarized posterior class.
 #' @export
 createPosterior <- function(prior, likelihood){

BayesianTools/R/classPrior.R

@@ -13,6 +13,7 @@
 #'          \code{\link{createUniformPrior}} \cr
 #'          \code{\link{createTruncatedNormalPrior}}\cr
 #'          \code{\link{createBayesianSetup}}\cr
+#'          \code{\link{print.prior}}\cr
 #' @example /inst/examples/createPrior.R
 createPrior <- function(density = NULL, sampler = NULL, lower = NULL, upper = NULL, best = NULL){
 
@@ -246,10 +247,13 @@ createPriorDensity <- function(sampler, method = "multivariate", eps = 1e-10, lo
 
 
 
+#' Print object of prior class
+#' @description print function for objects of class prior
+#' @param x object of a prior
+#' @param ... additional parameters
 #' @author Maximilian Pichler
-
 #' @export
-
+#' @seealso \code{\link{createPrior}}
 print.prior <- function(x, ...){
   cat('Prior: \n\n')
 

BayesianTools/R/classSMCSamplerList.R

@@ -15,21 +15,23 @@ createSmcSamplerList <- function(...){
 
 #' @method summary smcSamplerList
 #' @author Florian Hartig
-#' @export
+#' @describeIn summary.smcSampler 
 summary.smcSamplerList <- function(object, ...){
   sample = getSample(object, parametersOnly = T, ...)
   summary(sample)
 }
 
 #' @method print smcSamplerList
 #' @author Florian Hartig
+#' @describeIn print.smcSampler 
 #' @export
 print.smcSamplerList <- function(x, ...){
   print("smcSamplerList - you can use the following methods to summarize, plot or reduce this class:")
   print(methods(class ="smcSamplerList"))
 }
 
 #' @method plot smcSamplerList
+#' @describeIn plot.smcSampler 
 #' @export
 plot.smcSamplerList <- function(x, ...){
   marginalPlot(x, ...)

BayesianTools/R/classSmcSampler.R

@@ -1,3 +1,8 @@
+# S3 Functions for class 'smcSampler'
+
+
+
+#' @rdname getSample
 #' @author Florian Hartig
 #' @export
 getSample.smcSampler <- function(sampler, parametersOnly = T, coda = F, start = 1, end = NULL, thin = 1, numSamples = NULL, whichParameters = NULL, reportDiagnostics = FALSE, ...){
@@ -43,6 +48,11 @@ getSample.smcSampler <- function(sampler, parametersOnly = T, coda = F, start =
   } else return(out)
 }
 
+#' Summary for class 'smcSampler'
+#' @description
+#' Creates a summary table of a 'smcSampler' output
+#' @param object object of class smcSampler or smcSamplerList
+#' @param ... additional parameters
 #' @author Florian Hartig
 #' @method summary smcSampler
 #' @export
@@ -52,12 +62,22 @@ summary.smcSampler<- function(object, ...){
   summary(getSample(sampler, ...))
 }
 
+#' Plots of smcSampler output
+#' @description
+#' Plots smcSampler output
+#' @param x object of class mcmcSampler or mcmcSamplerList
+#' @param ... additional options passed to tracePlot
 #' @method plot smcSampler
 #' @export
 plot.smcSampler<- function(x, ...){
   marginalPlot(x, ...)
 }
 
+#' Print of smcSampler output
+#' @description
+#' Print smcSampler output
+#' @param x object of class mcmcSampler or mcmcSamplerList
+#' @param ... additional options 
 #' @author Florian Hartig
 #' @method print smcSampler
 #' @export

BayesianTools/R/marginalLikelihood.R

@@ -1,14 +1,12 @@
-
 # Motivation for this functions from 
 # https://radfordneal.wordpress.com/2008/08/17/the-harmonic-mean-of-the-likelihood-worst-monte-carlo-method-ever/
 # https://gist.github.com/gaberoo/4619102
 
 
-#  ' @export
-#marginalLikelihood <- function(x,lik,V,sampler$setup$likelihood$density,sampler$setup$prior$density,..., num.samples=1000,log=TRUE) UseMethod("marginalLikelihood")
+# @export
+# marginalLikelihood <- function(x,lik,V,sampler$setup$likelihood$density,sampler$setup$prior$density,..., num.samples=1000,log=TRUE) UseMethod("marginalLikelihood")
 
-#' Calcluated the marginal likelihood from a set of MCMC samples
-#' @export
+#' Calculated the marginal likelihood from a set of MCMC samples
 #' @author Florian Hartig
 #' @param sampler an MCMC or SMC sampler or list, or for method "Prior" also a BayesianSetup
 #' @param numSamples number of samples to use. How this works, and if it requires recalculating the likelihood, depends on the method
@@ -44,6 +42,7 @@
 #' Dormann et al. 2018. Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference. Ecological Monographs
 #' 
 #' @seealso \code{\link{WAIC}}, \code{\link{DIC}}, \code{\link{MAP}}
+#' @export
 marginalLikelihood <- function(sampler, numSamples = 1000, method = "Chib", ...){
 
 

BayesianTools/R/mcmcRun.R

@@ -28,7 +28,9 @@
 #' 
 #' setting startValue for sampling with nrChains > 1 : if you want to provide different start values for the different chains, provide them as a list
 #' 
-#' @return The function returns an object of class mcmcSampler (if one chain is run) or mcmcSamplerList. Both have the superclass bayesianOutput. It is possible to extract the samples as a coda object or matrix with \code{\link{getSample}}. 
+#' @return The function returns an object of class mcmcSampler (if one chain is run) or mcmcSamplerList. Both have the superclass bayesianOutput. It is possible to extract the samples as a coda object or matrix with \code{\link{getSample}}. Other S3 classes that are implemented include \code{\link{summary.mcmcSampler}} or \code{\link{summary.mcmcSamplerList}}, \code{\link{print.mcmcSampler}} or \code{\link{print.mcmcSamplerList}}, \code{\link{plot.mcmcSampler}} or \code{\link{plot.mcmcSamplerList}}
+#' 
+#' 
 #' It is also possible to summarize the posterior as a new prior via \code{\link{createPriorDensity}}.
 #' @example /inst/examples/mcmcRun.R
 #' @seealso \code{\link{createBayesianSetup}}