postproc fix

RBBGCMuso/R/postProcString.R

@@ -1,3 +1,7 @@
+#' postProcMuso
+#' 
+#' This is a function wich provides some minimal post processing capabilities
+#' @keywords internal
 postProcMuso  <- function(modelData, procString){
     modelDat <- modelData[,-(1:4)]
     cNames <- colnames(modelData)

RBBGCMuso/man/musoMonte.Rd

@@ -8,7 +8,8 @@ musoMonte(settings = NULL, parameters = NULL, inputDir = "./",
   outLoc = "./calib", iterations = 10, preTag = "mont-",
   outputType = "moreCsv", fun = mean, varIndex = 1, outVars = NULL,
   silent = TRUE, skipSpinup = TRUE, debugging = FALSE,
-  keepEpc = FALSE, constrains = NULL, ...)
+  keepEpc = FALSE, constrains = NULL, skipZero = TRUE,
+  postProcString = NULL, ...)
 }
 \arguments{
 \item{settings}{A list of environmental variables for the Monte Carlo experiment. These settings are generated by the setupMuso function. By default the settings parameter is generated automatically.}

RBBGCMuso/man/musoSensi.Rd

@@ -9,7 +9,8 @@ musoSensi(monteCarloFile = NULL, parameters = NULL, settings = NULL,
   iterations = 30, preTag = "mont-", outputType = "moreCsv",
   fun = mean, varIndex = 1, outputFile = "sensitivity.csv",
   plotName = "sensitivity.png", plotTitle = "Sensitivity",
-  skipSpinup = TRUE, dpi = 300)
+  skipSpinup = TRUE, skipZero = TRUE, postProcString = NULL,
+  dpi = 300)
 }
 \arguments{
 \item{parameters}{This is a dataframe (heterogen data-matrix), which first column is the name of the parameters, the second is a numeric vector of the rownumbers of the given variable in the epc-fie, the last two column consist the endpont of the parameter-ranges, where the parameters will be randomized.}