big changes + version upgrade
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hollorol
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@ -1,6 +1,6 @@
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Package: RBBGCMuso
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Title: An R package for BiomeBGC-MuSo ecosystem modelling
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Version: 0.4.0.0-2
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Version: 0.5.0.0-0
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Authors@R: person("Roland", "Hollo's", , "hollorol@gmail.com", role = c("aut", "cre"))
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Description: What the package does (one paragraph).
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Depends: R (>= 2.10)
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@ -22,6 +22,7 @@ musoMonte <- function(settings=NULL,
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outputType = "moreCsv",
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fun=mean,
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varIndex = 1,
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silent = TRUE,
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...){
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outLocPlain <- basename(outLoc)
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@ -75,8 +76,12 @@ musoMonte <- function(settings=NULL,
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## run.
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preservedEpc <- matrix(nrow = (iterations +1 ), ncol = npar)
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preservedEpc[1,] <- origEpc
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colnames(preservedEpc) <- Otable[[1]][,1]
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Otable[[1]][,1] <- (as.character(Otable[[1]][,1]))
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for(i in parameters[,2]){
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Otable[[1]][Otable[[1]][,2]==i,1] <- as.character(parameters[parameters[,2]==i,1])
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}
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colnames(preservedEpc) <- Otable[[1]][,1]
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preservedEpc <- cbind(preservedEpc,rep(NA,(iterations+1)))
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colnames(preservedEpc)[(npar+1)] <- "y"
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## Save the backupEpc, while change the settings
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@ -86,9 +91,10 @@ musoMonte <- function(settings=NULL,
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## Creating function for generating separate
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## csv files for each run
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progBar <- txtProgressBar(1,iterations,style=3)
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moreCsv <- function(){
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a <- numeric(iterations+1)
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tempData <- calibMuso(settings, debugging = "stamplog", parameters = origEpc,keepEpc = TRUE)
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tempData <- calibMuso(settings, debugging = "stamplog", parameters = origEpc,keepEpc = TRUE,silent = silent)
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a[1] <- tryCatch(fun(tempData[,varIndex]),error=function(e){return(NA)})
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preservedEpc[1,(npar+1)] <- a[1]
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write.table(t(preservedEpc[1,]),row.names = FALSE,"preservedEpc.csv",sep=",")
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@ -99,12 +105,15 @@ musoMonte <- function(settings=NULL,
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exportName <- paste0(preTag,(i+1),".csv")
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tempData <- calibMuso(settings,debugging = "stamplog",
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parameters = parVar,
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keepEpc = TRUE)
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keepEpc = TRUE,
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silent=silent)
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write.csv(x=tempData,file=exportName)
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preservedEpc[(i+1),(npar+1)] <- a[i+1]<- tryCatch(fun(tempData[,varIndex]),error=function(e){return(NA)})
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write.table(t(preservedEpc[(i+1),]),file="preservedEpc.csv",row.names=FALSE,col.names=FALSE, append=TRUE,sep=",")
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setTxtProgressBar(progBar,i)
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}
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cat("\n")
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return(preservedEpc)
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}
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@ -25,46 +25,19 @@ musoSensi <- function(monteCarloFile = NULL,
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inputDir = "./",
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outLoc = "./calib",
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iterations = 30,
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preTag = "mount-",
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preTag = "mont-",
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outputType = "moreCsv",
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fun = mean,
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varIndex = 1,
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outputFile = "sensitivity.csv",
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plotName = "sensitivity.jpg"){
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if(is.null(monteCarloFile)){
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M <- musoMonte(parameters = parameters,
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settings = settings,
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inputDir = inputDir,
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outLoc = outLoc,
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iterations = iterations,
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preTag = preTag,
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outputType = outputType,
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fun = fun,
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varIndex = varIndex
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)
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doSensi <- function(M){
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npar <- ncol(M)-1
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M <- M[which(!is.na(M$y)),]
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y <- M[,(npar+1)]
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M <- apply(M[,1:npar],2,function(x){x-mean(x)})
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varNames<- colnames(M)[1:npar]
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w <- lm(y~M)$coefficients[-1]
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Sv <- apply(M,2,var)
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overalVar <- sum(Sv^2*w^2)
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S=numeric(npar)
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for(i in 1:npar){
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S[i] <- ((w[i]^2*Sv[i]^2)/overalVar)*100
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}
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write.csv(file = outputFile, x = S)
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names(S)<-varNames
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barplot(S,las=2)
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return(S)
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} else {
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M <- read.csv(monteCarloFile)
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npar <- ncol(M)-1
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M <- M[which(!is.na(M$y)),]
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M <- M[which(!is.na(M[,"y"])),]
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y <- M[,(npar+1)]
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M <- M[,colnames(M) %in% parameters[,1]]
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npar <- ncol(M)
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M <- apply(M[,1:npar],2,function(x){x-mean(x)})
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varNames<- colnames(M)[1:npar]
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w <- lm(y~M)$coefficients[-1]
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@ -79,4 +52,24 @@ musoSensi <- function(monteCarloFile = NULL,
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barplot(S,las=2)
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return(S)
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}
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if(is.null(monteCarloFile)){
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M <- musoMonte(parameters = parameters,
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settings = settings,
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inputDir = inputDir,
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outLoc = outLoc,
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iterations = iterations,
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preTag = preTag,
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outputType = outputType,
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fun = fun,
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varIndex = varIndex
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)
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return(doSensi(M))
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} else {
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M <- read.csv(monteCarloFile)
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return(doSensi(M))
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}
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}
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@ -6,7 +6,7 @@
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\usage{
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musoMonte(settings = NULL, parameters, inputDir = "./",
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outLoc = "./calib", iterations = 10, preTag = "mont-",
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outputType = "moreCsv", fun = mean, varIndex = 1, ...)
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outputType = "moreCsv", fun = mean, varIndex = 1, silent = TRUE, ...)
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}
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\arguments{
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\item{settings}{A list of montecarlos environmental variables. It is generated by the setupMuso() function. In default the settings parameter is generated automatically.}
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@ -6,7 +6,7 @@
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\usage{
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musoSensi(monteCarloFile = NULL, parameters, settings = NULL,
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inputDir = "./", outLoc = "./calib", iterations = 30,
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preTag = "mount-", outputType = "moreCsv", fun = mean, varIndex = 1,
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preTag = "mont-", outputType = "moreCsv", fun = mean, varIndex = 1,
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outputFile = "sensitivity.csv", plotName = "sensitivity.jpg")
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}
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\arguments{
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