R/OrdinationPlots.R
In TimKroeger13/ProjectMultiLake: RTools used by the Alfred wegner institut
Defines functions
Ordination
Documented in
Ordination
#'Ordination
#'@description
#'Plots Ordinations Plots.
#'@param data List of data generates by the Multivar function.
#'@param minimumRowsAfterCutOutMaxAge minimum rows count after filtering.
#'@param allspan The span for all loess functions.
#'@param MaxAge The max Age where to cut the Data.
#'@importFrom grDevices rainbow
#'@importFrom graphics lines points text legend barplot par axis
#'@importFrom stats na.omit
#'@importFrom grDevices dev.off pdf
#'@export
#'@return nothing.
#'@author Tim Kroeger
#'@note This function has only been developed for the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research and should therefore only be used in combination with their database.
Ordination = function(data, minimumRowsAfterCutOutMaxAge = 12, allspan = 1, MaxAge = 20000){
#new
orginalWorkingDirectoryPath=getwd()
getwdTry <- try(setwd(paste(getwd(),.Platform[2],paste("Out_",data$Filter,sep = ""),sep="")),silent = TRUE)
if(class(getwdTry) == "try-error"){
dir.create(paste(getwd(),.Platform[2],paste("Out_",data$Filter,sep = ""),sep=""))
setwd(paste(getwd(),.Platform[2],paste("Out_",data$Filter,sep = ""),sep=""))
}
CutOutMaxAge = function(DataVector, Cutage, minimumRowsAfterCutOutMaxAge){
FittingAges = as.numeric(row.names(DataVector))<=Cutage
if(sum(FittingAges)>minimumRowsAfterCutOutMaxAge){
DataVector=cbind(as.numeric(row.names(DataVector))[FittingAges],DataVector[FittingAges])
return(DataVector)
}else{
return(NULL)
}
}
CutOutMaxAgeForInterpolatedData = function(DataVector, Cutage, minimumRowsAfterCutOutMaxAge){
FittingAges = as.numeric(DataVector[,1])<=Cutage
if(sum(FittingAges)>minimumRowsAfterCutOutMaxAge){
DataVector=cbind(DataVector[FittingAges,1],DataVector[FittingAges,2])
return(DataVector)
}else{
return(NULL)
}
}
NormalizeRichnessDataTable = function(RichnessData){
for (i in 1:dim(RichnessData)[2]){
RichnessData[,i] = (RichnessData[,i]-min(RichnessData[,i]))/(max(RichnessData[,i])-min(RichnessData[,i]))
}
return(RichnessData)
}
AgeNormailzer = function(RichnessData){
RichnessData = RichnessData[,1]
return((RichnessData-min(RichnessData))/(max(RichnessData)-min(RichnessData)))
}
DeleteMeanNAS = function(MeanMatrix){
MeanMatrixCounter = 0
while (MeanMatrixCounter<dim(MeanMatrix)[1]) {
MeanMatrixCounter = MeanMatrixCounter+1
if(MeanMatrix[MeanMatrixCounter,2]==0){
MeanMatrix = MeanMatrix[-MeanMatrixCounter,]
MeanMatrixCounter = MeanMatrixCounter-1
}
}
return(MeanMatrix)
}
################################################################################
################################# Discription ##################################
################################################################################
Allcolor = rainbow(dim(data$CoreList)[1])
AllDiatomsNames = ls(data$Diatom)
AllCarbonsNames = ls(data$Carbon)
AllTRACENames = ls(data$TRACE)
################################################################################
##################################### MDS ######################################
################################################################################
PlotsVariantsLoess = c("Normal","Loess")
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
if(VariantsTransform == "Non Transformed"){
pdf(paste("Z_old MDS_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
#Plot Limits
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(rownames(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old MDS | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
if(VariantsTransform == "Transformed"){
pdf(paste("Z_old MDS_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(rownames(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old MDS | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
################################################################################
################################### Richness ###################################
################################################################################
nameLoess="Loess_invsimpson"
nameNormal="invsimpson"
minlegth = 10
PlotsVariantsLoess = c("Normal","Loess")
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
if(VariantsTransform == "Non Transformed"){
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
if(!is.null(Values)){
pdf(paste("Z_old N2_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
#Plot Limits
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]]) # $nMDS$Dim1
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
valueNames = as.numeric(rownames(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old N2 | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
}
dev.off()
}
}
if(VariantsTransform == "Transformed"){
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
if(!is.null(Values)){
pdf(paste("Z_old N2_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
valueNames = as.numeric(rownames(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old N2 | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
}
dev.off()
}
}
}
}
################################################################################
##################################### Clima ####################################
################################################################################
PlotsVariantsLoess = c("Normal","Loess")
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
if(VariantsTransform == "Non Transformed"){
pdf(paste("PalioClima_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
#Plot Limits
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){ # length(data$Diatom)
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Palio Clima | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[1,1]+distance,
Values[1,2],
label=RID,
col="white",
cex=1.2)
text(Values[1,1]+distance,
Values[1,2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
if(VariantsTransform == "Transformed"){
pdf(paste("PalioClima_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Palio Clima | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[1,1]+distance,
Values[1,2],
label=RID,
col="white",
cex=1.2)
text(Values[1,1]+distance,
Values[1,2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
################################################################################
#################################### Carbon ####################################
################################################################################
nameLoess="Loess_invsimpson"
nameNormal="invsimpson"
PlotsVariantsLoess = c("Normal","Loess")
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
if(VariantsTransform == "Non Transformed"){
pdf(paste("Z_old TOC_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
#Plot Limits
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC) # $Species_richness[[nameNormal]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old TOC | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){ #length(data$Diatom)
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
if(VariantsTransform == "Transformed"){
pdf(paste("Z_old TOC_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old TOC | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
#Legend
pdf("Legend.pdf",width=15,height=10)
# AllCoreList dim(data$CoreList)[1]
LegendsSplit = length(Allcolor)/3
SplitValue1 = ceiling(LegendsSplit)
SplitValue2 = ceiling(LegendsSplit*2)
SplitValue3 = length(Allcolor)
AllNamesPart1 = data$CoreList[,1][1:SplitValue1]
AllNamesPart2 = data$CoreList[,1][(SplitValue1+1):SplitValue2]
AllNamesPart3 = data$CoreList[,1][(SplitValue2+1):SplitValue3]
plot(NA,
ylim=c(0,1),
xlim=c(0,1),
ylab="",
xlab="",
main="Legend",
bty="n",
xaxt='n',
yaxt='n'
)
thickness = 1
legend(x = "left", legend = paste(1:SplitValue1,"-",AllNamesPart1), col = Allcolor[1:SplitValue1], lty = 1, lwd = 3, text.width=0.3, cex = thickness, bty="n") # bty="n"
legend(x = "center", legend = paste((SplitValue1+1):SplitValue2,"-",AllNamesPart2), col = Allcolor[(SplitValue1+1):SplitValue2], lty = 1, lwd = 3, text.width=0.3, cex = thickness, bty="n") # bty="n"
legend(x = "right", legend = paste((SplitValue2+1):SplitValue3,"-",AllNamesPart3), col = Allcolor[(SplitValue2+1):SplitValue3], lty = 1, lwd = 3, text.width=0.3, cex = thickness, bty="n") # bty="n"
dev.off()
################################################################################
###################################### RoC #####################################
################################################################################
pdf("RoC.pdf",width=15,height=10)
RocMatrix = data$RocMatrix
#Cut Data after x years
CutValue = MaxAge
RocMatrix=RocMatrix[RocMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
RocMatrix = DeleteMeanNAS(RocMatrix)
Xmin = min(RocMatrix[,1])
Xmax = max(RocMatrix[,1])
Ymin = min(RocMatrix[,3], na.rm = T)
Ymax = max(RocMatrix[,4], na.rm = T)
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(RocMatrix)[1])
P_color[] = "red"
P_color[RocMatrix[,5]<P_value] = "green"
P_color[RocMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("RoC Mean",sep="")
)
#lines(RocMatrix[,1],RocMatrix[,2], col= "blue")
#points(RocMatrix[,1],RocMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(RocMatrix)[1]-1)){
lines(RocMatrix[i:(i+1),1],RocMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(RocMatrix[,1],RocMatrix[,3], col= "black",lty=3,lwd=2)
lines(RocMatrix[,1],RocMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# RoC ##################################
############################### vector ################################
################################################################################
pdf("Vector_RoC.pdf",width=15,height=10)
RocMatrix = data$Vector_RocMatrix
#Cut Data after x years
CutValue = MaxAge
RocMatrix=RocMatrix[RocMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
RocMatrix = DeleteMeanNAS(RocMatrix)
Xmin = min(RocMatrix[,1])
Xmax = max(RocMatrix[,1])
Ymin = min(na.omit(RocMatrix[,3]))
Ymax = max(na.omit(RocMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(RocMatrix)[1])
P_color[] = "red"
P_color[RocMatrix[,5]<P_value] = "green"
P_color[RocMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Normal - RoC Mean",sep="")
)
for (i in 1:(dim(RocMatrix)[1]-1)){
lines(RocMatrix[i:(i+1),1],RocMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(RocMatrix[,1],RocMatrix[,3], col= "black",lty=3,lwd=2)
lines(RocMatrix[,1],RocMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# RoC ##################################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_RoC_Transform.pdf",width=15,height=10)
RocMatrix = data$Vector_RocMatrix_transform
#Cut Data after x years
CutValue = MaxAge
RocMatrix=RocMatrix[RocMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
RocMatrix = DeleteMeanNAS(RocMatrix)
Xmin = min(RocMatrix[,1])
Xmax = max(RocMatrix[,1])
Ymin = min(na.omit(RocMatrix[,3]))
Ymax = max(na.omit(RocMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(RocMatrix)[1])
P_color[] = "red"
P_color[RocMatrix[,5]<P_value] = "green"
P_color[RocMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - RoC Mean",sep="")
)
for (i in 1:(dim(RocMatrix)[1]-1)){
lines(RocMatrix[i:(i+1),1],RocMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(RocMatrix[,1],RocMatrix[,3], col= "black",lty=3,lwd=2)
lines(RocMatrix[,1],RocMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################### Evenness ###################################
################################################################################
pdf("Evenness.pdf",width=15,height=10)
EvennessMatrix = data$EvennessMatrix
#Cut Data after x years
CutValue = MaxAge
EvennessMatrix=EvennessMatrix[EvennessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
EvennessMatrix = DeleteMeanNAS(EvennessMatrix)
Xmin = min(EvennessMatrix[,1])
Xmax = max(EvennessMatrix[,1])
Ymin = min(EvennessMatrix[,3])
Ymax = max(EvennessMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(EvennessMatrix)[1])
P_color[] = "red"
P_color[EvennessMatrix[,5]<P_value] = "green"
P_color[EvennessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Evenness Mean",CutValue,sep="")
)
#lines(EvennessMatrix[,1],EvennessMatrix[,2], col= "blue")
#points(EvennessMatrix[,1],EvennessMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(EvennessMatrix)[1]-1)){
lines(EvennessMatrix[i:(i+1),1],EvennessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(EvennessMatrix[,1],EvennessMatrix[,3], col= "black",lty=3,lwd=2)
lines(EvennessMatrix[,1],EvennessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
############################## Evenness ################################
############################### vector ################################
################################################################################
pdf("Vector_Evenness.pdf",width=15,height=10)
EvennessMatrix = data$Vector_Evenness
#Cut Data after x years
CutValue = MaxAge
EvennessMatrix=EvennessMatrix[EvennessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
EvennessMatrix = DeleteMeanNAS(EvennessMatrix)
Xmin = min(EvennessMatrix[,1])
Xmax = max(EvennessMatrix[,1])
Ymin = min(na.omit(EvennessMatrix[,3]))
Ymax = max(na.omit(EvennessMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(EvennessMatrix)[1])
P_color[] = "red"
P_color[EvennessMatrix[,5]<P_value] = "green"
P_color[EvennessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - evenness Mean",sep="")
)
for (i in 1:(dim(EvennessMatrix)[1]-1)){
lines(EvennessMatrix[i:(i+1),1],EvennessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(EvennessMatrix[,1],EvennessMatrix[,3], col= "black",lty=3,lwd=2)
lines(EvennessMatrix[,1],EvennessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
############################## Evenness ################################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_Evenness_Transform.pdf",width=15,height=10)
EvennessMatrix = data$Vector_Evenness_transform
#Cut Data after x years
CutValue = MaxAge
EvennessMatrix=EvennessMatrix[EvennessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
EvennessMatrix = DeleteMeanNAS(EvennessMatrix)
Xmin = min(EvennessMatrix[,1])
Xmax = max(EvennessMatrix[,1])
Ymin = min(na.omit(EvennessMatrix[,3]))
Ymax = max(na.omit(EvennessMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(EvennessMatrix)[1])
P_color[] = "red"
P_color[EvennessMatrix[,5]<P_value] = "green"
P_color[EvennessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - evenness Mean",sep="")
)
for (i in 1:(dim(EvennessMatrix)[1]-1)){
lines(EvennessMatrix[i:(i+1),1],EvennessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(EvennessMatrix[,1],EvennessMatrix[,3], col= "black",lty=3,lwd=2)
lines(EvennessMatrix[,1],EvennessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
############################### InverseSimpsion ################################
################################################################################
pdf("InverseSimpsion.pdf",width=15,height=10)
InverseSimpsionMatrix = data$InverseSimpsionMatrix
#Cut Data after x years
CutValue = MaxAge
InverseSimpsionMatrix=InverseSimpsionMatrix[InverseSimpsionMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
InverseSimpsionMatrix = DeleteMeanNAS(InverseSimpsionMatrix)
Xmin = min(InverseSimpsionMatrix[,1])
Xmax = max(InverseSimpsionMatrix[,1])
Ymin = min(InverseSimpsionMatrix[,3])
Ymax = max(InverseSimpsionMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(InverseSimpsionMatrix)[1])
P_color[] = "red"
P_color[InverseSimpsionMatrix[,5]<P_value] = "green"
P_color[InverseSimpsionMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("InverseSimpsion Mean",CutValue,sep="")
)
#lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,2], col= "blue")
#points(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(InverseSimpsionMatrix)[1]-1)){
lines(InverseSimpsionMatrix[i:(i+1),1],InverseSimpsionMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,3], col= "black",lty=3,lwd=2)
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
########################## InverseSimpsion #############################
############################### vector ################################
################################################################################
pdf("Vector_InverseSimpsion.pdf",width=15,height=10)
InverseSimpsionMatrix = data$Vector_InverseSimpsion
#Cut Data after x years
CutValue = MaxAge
InverseSimpsionMatrix=InverseSimpsionMatrix[InverseSimpsionMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
InverseSimpsionMatrix = DeleteMeanNAS(InverseSimpsionMatrix)
Xmin = min(InverseSimpsionMatrix[,1])
Xmax = max(InverseSimpsionMatrix[,1])
Ymin = min(na.omit(InverseSimpsionMatrix[,3]))
Ymax = max(na.omit(InverseSimpsionMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(InverseSimpsionMatrix)[1])
P_color[] = "red"
P_color[InverseSimpsionMatrix[,5]<P_value] = "green"
P_color[InverseSimpsionMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - InverseSimpsion Mean",sep="")
)
for (i in 1:(dim(InverseSimpsionMatrix)[1]-1)){
lines(InverseSimpsionMatrix[i:(i+1),1],InverseSimpsionMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,3], col= "black",lty=3,lwd=2)
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
########################## InverseSimpsion #############################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_InverseSimpsion_Transform.pdf",width=15,height=10)
InverseSimpsionMatrix = data$Vector_InverseSimpsion_transform
#Cut Data after x years
CutValue = MaxAge
InverseSimpsionMatrix=InverseSimpsionMatrix[InverseSimpsionMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
InverseSimpsionMatrix = DeleteMeanNAS(InverseSimpsionMatrix)
Xmin = min(InverseSimpsionMatrix[,1])
Xmax = max(InverseSimpsionMatrix[,1])
Ymin = min(na.omit(InverseSimpsionMatrix[,3]))
Ymax = max(na.omit(InverseSimpsionMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(InverseSimpsionMatrix)[1])
P_color[] = "red"
P_color[InverseSimpsionMatrix[,5]<P_value] = "green"
P_color[InverseSimpsionMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - InverseSimpsion Mean",sep="")
)
for (i in 1:(dim(InverseSimpsionMatrix)[1]-1)){
lines(InverseSimpsionMatrix[i:(i+1),1],InverseSimpsionMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,3], col= "black",lty=3,lwd=2)
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
############################### SpeciesRichness ################################
################################################################################
pdf("SpeciesRichness.pdf",width=15,height=10)
SpeciesRichnessMatrix = data$SpeciesRichnessMatrix
#Cut Data after x years
CutValue = MaxAge
SpeciesRichnessMatrix=SpeciesRichnessMatrix[SpeciesRichnessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
SpeciesRichnessMatrix = DeleteMeanNAS(SpeciesRichnessMatrix)
Xmin = min(SpeciesRichnessMatrix[,1])
Xmax = max(SpeciesRichnessMatrix[,1])
Ymin = min(SpeciesRichnessMatrix[,3])
Ymax = max(SpeciesRichnessMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(SpeciesRichnessMatrix)[1])
P_color[] = "red"
P_color[SpeciesRichnessMatrix[,5]<P_value] = "green"
P_color[SpeciesRichnessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("SpeciesRichness Mean",CutValue,sep="")
)
for (i in 1:(dim(SpeciesRichnessMatrix)[1]-1)){
lines(SpeciesRichnessMatrix[i:(i+1),1],SpeciesRichnessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,3], col= "black",lty=3,lwd=2)
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
########################## SpeciesRichness #############################
############################### vector ################################
################################################################################
pdf("Vector_SpeciesRichness.pdf",width=15,height=10)
SpeciesRichnessMatrix = data$Vector_SpeciesRichness
#Cut Data after x years
CutValue = MaxAge
SpeciesRichnessMatrix=SpeciesRichnessMatrix[SpeciesRichnessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
SpeciesRichnessMatrix = DeleteMeanNAS(SpeciesRichnessMatrix)
Xmin = min(SpeciesRichnessMatrix[,1])
Xmax = max(SpeciesRichnessMatrix[,1])
Ymin = min(na.omit(SpeciesRichnessMatrix[,3]))
Ymax = max(na.omit(SpeciesRichnessMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(SpeciesRichnessMatrix)[1])
P_color[] = "red"
P_color[SpeciesRichnessMatrix[,5]<P_value] = "green"
P_color[SpeciesRichnessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - SpeciesRichness Mean",sep="")
)
for (i in 1:(dim(SpeciesRichnessMatrix)[1]-1)){
lines(SpeciesRichnessMatrix[i:(i+1),1],SpeciesRichnessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,3], col= "black",lty=3,lwd=2)
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
########################## SpeciesRichness #############################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_SpeciesRichness_Transform.pdf",width=15,height=10)
SpeciesRichnessMatrix = data$Vector_SpeciesRichness_transform
#Cut Data after x years
CutValue = MaxAge
SpeciesRichnessMatrix=SpeciesRichnessMatrix[SpeciesRichnessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
SpeciesRichnessMatrix = DeleteMeanNAS(SpeciesRichnessMatrix)
Xmin = min(SpeciesRichnessMatrix[,1])
Xmax = max(SpeciesRichnessMatrix[,1])
Ymin = min(na.omit(SpeciesRichnessMatrix[,3]))
Ymax = max(na.omit(SpeciesRichnessMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(SpeciesRichnessMatrix)[1])
P_color[] = "red"
P_color[SpeciesRichnessMatrix[,5]<P_value] = "green"
P_color[SpeciesRichnessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - SpeciesRichness Mean",sep="")
)
for (i in 1:(dim(SpeciesRichnessMatrix)[1]-1)){
lines(SpeciesRichnessMatrix[i:(i+1),1],SpeciesRichnessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,3], col= "black",lty=3,lwd=2)
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
##################################### MDS ######################################
################################################################################
pdf("MDS.pdf",width=15,height=10)
MDSMatrix = data$MDSMatrix
#Cut Data after x years
CutValue = MaxAge
MDSMatrix=MDSMatrix[MDSMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
MDSMatrix = DeleteMeanNAS(MDSMatrix)
Xmin = min(MDSMatrix[,1])
Xmax = max(MDSMatrix[,1])
Ymin = min(MDSMatrix[,3])
Ymax = max(MDSMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(MDSMatrix)[1])
P_color[] = "red"
P_color[MDSMatrix[,5]<P_value] = "green"
P_color[MDSMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("MDS Mean",CutValue,sep="")
)
#lines(MDSMatrix[,1],MDSMatrix[,2], col= "blue")
#points(MDSMatrix[,1],MDSMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(MDSMatrix)[1]-1)){
lines(MDSMatrix[i:(i+1),1],MDSMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(MDSMatrix[,1],MDSMatrix[,3], col= "black",lty=3,lwd=2)
lines(MDSMatrix[,1],MDSMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################ MDS ###################################
############################### vector ################################
################################################################################
pdf("Vector_MDS.pdf",width=15,height=10)
MDSMatrix = data$Vector_MDS
#Cut Data after x years
CutValue = MaxAge
MDSMatrix=MDSMatrix[MDSMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
MDSMatrix = DeleteMeanNAS(MDSMatrix)
Xmin = min(MDSMatrix[,1])
Xmax = max(MDSMatrix[,1])
Ymin = min(na.omit(MDSMatrix[,3]))
Ymax = max(na.omit(MDSMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(MDSMatrix)[1])
P_color[] = "red"
P_color[MDSMatrix[,5]<P_value] = "green"
P_color[MDSMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - MDS Mean",sep="")
)
for (i in 1:(dim(MDSMatrix)[1]-1)){
lines(MDSMatrix[i:(i+1),1],MDSMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(MDSMatrix[,1],MDSMatrix[,3], col= "black",lty=3,lwd=2)
lines(MDSMatrix[,1],MDSMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# MDS ##################################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_MDS_Transform.pdf",width=15,height=10)
MDSMatrix = data$Vector_MDS_transform
#Cut Data after x years
CutValue = MaxAge
MDSMatrix=MDSMatrix[MDSMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
MDSMatrix = DeleteMeanNAS(MDSMatrix)
Xmin = min(MDSMatrix[,1])
Xmax = max(MDSMatrix[,1])
Ymin = min(na.omit(MDSMatrix[,3]))
Ymax = max(na.omit(MDSMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(MDSMatrix)[1])
P_color[] = "red"
P_color[MDSMatrix[,5]<P_value] = "green"
P_color[MDSMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - MDS Mean",sep="")
)
for (i in 1:(dim(MDSMatrix)[1]-1)){
lines(MDSMatrix[i:(i+1),1],MDSMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(MDSMatrix[,1],MDSMatrix[,3], col= "black",lty=3,lwd=2)
lines(MDSMatrix[,1],MDSMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
##################################### TOC ######################################
################################################################################
pdf("TOC.pdf",width=15,height=10)
TOCMatrix = data$TOCMatrix
#Cut Data after x years
CutValue = MaxAge
TOCMatrix=TOCMatrix[TOCMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
TOCMatrix = DeleteMeanNAS(TOCMatrix)
Xmin = min(TOCMatrix[,1])
Xmax = max(TOCMatrix[,1])
Ymin = min(TOCMatrix[,3])
Ymax = max(TOCMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(TOCMatrix)[1])
P_color[] = "red"
P_color[TOCMatrix[,5]<P_value] = "green"
P_color[TOCMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("TOC Mean",CutValue,sep="")
)
#lines(TOCMatrix[,1],TOCMatrix[,2], col= "blue")
#points(TOCMatrix[,1],TOCMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(TOCMatrix)[1]-1)){
lines(TOCMatrix[i:(i+1),1],TOCMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(TOCMatrix[,1],TOCMatrix[,3], col= "black",lty=3,lwd=2)
lines(TOCMatrix[,1],TOCMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# TOC ##################################
############################### vector ################################
################################################################################
pdf("Vector_TOC.pdf",width=15,height=10)
TOCMatrix = data$Vector_TOC
#Cut Data after x years
CutValue = MaxAge
TOCMatrix=TOCMatrix[TOCMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
TOCMatrix = DeleteMeanNAS(TOCMatrix)
Xmin = min(TOCMatrix[,1])
Xmax = max(TOCMatrix[,1])
Ymin = min(na.omit(TOCMatrix[,3]))
Ymax = max(na.omit(TOCMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(TOCMatrix)[1])
P_color[] = "red"
P_color[TOCMatrix[,5]<P_value] = "green"
P_color[TOCMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - TOC Mean",sep="")
)
for (i in 1:(dim(TOCMatrix)[1]-1)){
lines(TOCMatrix[i:(i+1),1],TOCMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(TOCMatrix[,1],TOCMatrix[,3], col= "black",lty=3,lwd=2)
lines(TOCMatrix[,1],TOCMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# TOC ##################################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_TOC_Transform.pdf",width=15,height=10)
TOCMatrix = data$Vector_TOC_transform
#Cut Data after x years
CutValue = MaxAge
TOCMatrix=TOCMatrix[TOCMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
TOCMatrix = DeleteMeanNAS(TOCMatrix)
Xmin = min(TOCMatrix[,1])
Xmax = max(TOCMatrix[,1])
Ymin = min(na.omit(TOCMatrix[,3]))
Ymax = max(na.omit(TOCMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(TOCMatrix)[1])
P_color[] = "red"
P_color[TOCMatrix[,5]<P_value] = "green"
P_color[TOCMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - TOC Mean",sep="")
)
for (i in 1:(dim(TOCMatrix)[1]-1)){
lines(TOCMatrix[i:(i+1),1],TOCMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(TOCMatrix[,1],TOCMatrix[,3], col= "black",lty=3,lwd=2)
lines(TOCMatrix[,1],TOCMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
#################################### TRACE #####################################
################################################################################
TRACEMeanPlot= function(TraceName = "UnknownTrace"){
pdf(paste("TRACE_",TraceName,".pdf",sep=""),width=15,height=10)
TRACEMatrix = data$TRACEMatrix[[TraceName]]
#Cut Data after x years
CutValue = MaxAge
TRACEMatrix=TRACEMatrix[TRACEMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
TRACEMatrix = DeleteMeanNAS(TRACEMatrix)
Xmin = min(TRACEMatrix[,1])
Xmax = max(TRACEMatrix[,1])
Ymin = min(TRACEMatrix[,3])
Ymax = max(TRACEMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(TRACEMatrix)[1])
P_color[] = "red"
P_color[TRACEMatrix[,5]<P_value] = "green"
P_color[TRACEMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("TRACE Mean ",TraceName," ",CutValue,sep="")
)
#lines(TRACEMatrix[,1],TRACEMatrix[,2], col= "blue")
#points(TRACEMatrix[,1],TRACEMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(TRACEMatrix)[1]-1)){
lines(TRACEMatrix[i:(i+1),1],TRACEMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(TRACEMatrix[,1],TRACEMatrix[,3], col= "black",lty=3,lwd=2)
lines(TRACEMatrix[,1],TRACEMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
}
TRACEMeanPlot(TraceName = "JJA_mean")
TRACEMeanPlot(TraceName = "DJF_mean")
TRACEMeanPlot(TraceName = "hydrological_mean")
################################################################################
################################ Evenness Solo #################################
################################################################################
ImportVersions = c("evenness","Cut_evenness")
for (IV in ImportVersions){
PlotsVariantsLoess = c("Normal","Loess") # "Normal","Loess"
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
################################################################################
################################### RoC Solo ###################################
################################################################################
ImportVersions = c("RoC","Cut_RoC")
for (IV in ImportVersions){
PlotsVariantsLoess = c("Normal","Loess") # "Normal","Loess"
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
##############################################################################
############################## Correction Output #############################
##############################################################################
CorrectionPlots(data = data, AllDiatomsNames = AllDiatomsNames, MaxAge = MaxAge)
##############################################################################
############################ Inverse Simpson Solo ############################
##############################################################################
ImportVersions = c("inverseSimpsionData","Cut_inverseSimpsionData")
for (IV in ImportVersions){
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
PointValues = data$Diatom[[DiatomsNames]]$Species_richness$invsimpson
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
points(as.numeric(row.names(PointValues)),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
##############################################################################
########################### Species Richness Solo ############################
##############################################################################
ImportVersions = c("speciesRichnessData","Cut_speciesRichnessData")
for (IV in ImportVersions){
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
PointValues = data$Diatom[[DiatomsNames]]$Species_richness$richness
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
points(as.numeric(row.names(PointValues)),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
##############################################################################
################################## MDS Solo ##################################
##############################################################################
ImportVersions = c("MDS","Cut_MDS")
for (IV in ImportVersions){
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
PointValues = data$Diatom[[DiatomsNames]]$nMDS$Dim1
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
points(as.numeric(row.names(PointValues)),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values, MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
##############################################################################
################################## TOC Solo ##################################
##############################################################################
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste("TOC__",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Carbon)){
CarbonsNames = AllCarbonsNames[i]
Values = data$Carbon[[CarbonsNames]]$TOC
RID = which(data$CoreList[,1]==CarbonsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("TOC | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Carbon)){
CarbonsNames = AllCarbonsNames[i]
Values = data$Carbon[[CarbonsNames]]$TOC
RID = which(data$CoreList[,1]==CarbonsNames)
PointValues = data$Carbon[[CarbonsNames]]$rawData$TOC
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
points(as.numeric(data$Carbon[[CarbonsNames]]$rawData$depth),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Carbon)){
CarbonsNames = AllCarbonsNames[i]
Values = data$Carbon[[CarbonsNames]]$TOC
RID = which(data$CoreList[,1]==CarbonsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values, MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
##############################################################################
################################# TRACE Solo #################################
##############################################################################
TRACESoloPlot= function(TraceType = "UnknownTrace"){
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste("TRACE_",TraceType," ",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$TRACE)){
TRACEsNames = AllTRACENames[i]
Values = data$TRACE[[TRACEsNames]][[TraceType]]
RID = which(data$CoreList[,1]==TRACEsNames)
if(length(RID)>0){
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("TRACE | ",TraceType," ",VariantsTransform,sep="")
)
for (i in 1:length(data$TRACE)){
TRACEsNames = AllTRACENames[i]
Values = data$TRACE[[TRACEsNames]][[TraceType]]
RID = which(data$CoreList[,1]==TRACEsNames)
#PointValues = data$TRACE[[TRACEsNames]]$rawData$TRACE
if(length(RID)>0){
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
#PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
#points(as.numeric(data$TRACE[[TRACEsNames]]$rawData$depth),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
}
#Plot Numbers
for (i in 1:length(data$TRACE)){
TRACEsNames = AllTRACENames[i]
Values = data$TRACE[[TRACEsNames]][[TraceType]]
RID = which(data$CoreList[,1]==TRACEsNames)
if(length(RID)>0){
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values, MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
}
dev.off()
}
}
TRACESoloPlot(TraceType = "JJA_mean")
TRACESoloPlot(TraceType = "DJF_mean")
TRACESoloPlot(TraceType = "hydrological_mean")
################################################################################
################################# StressPlot ###################################
################################################################################
pdf("Stress.pdf",width=15,height=10)
DiatomNames = ls(data$Diatom)
LakeType = vector("character",length(DiatomNames))
Stress = vector("numeric",length(DiatomNames))
Names = DiatomNames
counter = 0
for (i in 1:length(DiatomNames)){
counter = counter+1
if (!sum(data$LakeData$CoreID==DiatomNames[counter])==0){
StressValue = data$Diatom[[DiatomNames[counter]]]$nMDS$Stress
if(!is.null(StressValue)){
LakeType[counter] = data$LakeDat$LakeType[which(data$LakeData$CoreID==DiatomNames[counter])]
Stress[counter] = StressValue
}else{
LakeType = LakeType[-counter]
Stress = Stress[-counter]
Names = Names[-counter]
DiatomNames = DiatomNames[-counter]
counter=counter-1
}
}else{
LakeType = LakeType[-counter]
Stress = Stress[-counter]
Names = Names[-counter]
DiatomNames = DiatomNames[-counter]
counter=counter-1
}
}
names(Stress) = Names
names(LakeType) = Names
Stress = Stress[!LakeType == "-"]
LakeType = LakeType[!LakeType == "-"]
sortStress = Stress[order(Stress,decreasing = F)]
sortLakeType = LakeType[order(Stress,decreasing = F)]
LakeFactor = as.factor(LakeType)
color = rainbow(length(levels(LakeFactor)))
colorVector <- vector( "character" , length(LakeType)[1])
for (c in 1:length(levels(LakeFactor))){
colorVector[which(LakeFactor == levels(LakeFactor)[c])] = color[c]
}
par(mar = c(5, 9, 5, 5))
par(mfrow = c(1,1))
barplot(sortStress,horiz = TRUE, las = 1, col = colorVector, main = "Stress Plot",
border = colorVector,
space=0.3)
legend(x = "bottom",
legend = levels(LakeFactor),
fill =color,
cex = 1.3,
bty = 'n',
inset = c(-0.15,0))
par(mar = c(5, 5, 5, 5))
par(mfrow = c(1,1))
dev.off()
#Distribution of measurements
#Diatom
pdf(paste("Diatom_DistributionOfMeasurements",".pdf",sep=""),width=15,height=15)
par(mar = c(5, 9, 5, 5))
par(mfrow = c(1,1))
plot(NA,
ylim=c(0,length(data$Diatom)),
xlim=c(20000,0),
ylab="",
xlab="Age",
main="Diatom | Distribution of measurements",
yaxt = "n"
)
axis(2, at = seq(0,length(data$Diatom)-1),
labels = AllDiatomsNames, las=2)
par(mar = c(5, 5, 5, 5))
par(mfrow = c(1,1))
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
RawData = data$Diatom[[DiatomsNames]]$rawData
Occurence = RawData[,1]
Occurence = Occurence[Occurence <= 20000]
points(Occurence,seq((i-1), (i-1), length.out=length(Occurence)),col="black", lwd=1,pch = 20)
}
dev.off()
#Carbon
pdf(paste("Carbon_DistributionOfMeasurements",".pdf",sep=""),width=15,height=15)
par(mar = c(5, 9, 5, 5))
par(mfrow = c(1,1))
plot(NA,
ylim=c(0,length(data$Carbon)),
xlim=c(20000,0),
ylab="",
xlab="Age",
main="Carbon | Distribution of measurement",
yaxt = "n"
)
axis(2, at = seq(0,length(data$Carbon)-1),
labels = AllCarbonsNames, las=2)
par(mar = c(5, 5, 5, 5))
par(mfrow = c(1,1))
for (i in 1:length(data$Carbon)){
DiatomsNames = AllCarbonsNames[i]
RawData = data$Carbon[[DiatomsNames]]$rawData
RawData = RawData[!is.na(RawData$TOC),]
Occurence = RawData[,1]
Occurence = Occurence[Occurence <= 20000]
points(Occurence,seq((i-1), (i-1), length.out=length(Occurence)),col="black", lwd=1,pch = 20)
}
dev.off()
setwd(orginalWorkingDirectoryPath)
}
TimKroeger13/ProjectMultiLake documentation built on March 18, 2022, 2:22 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions Ordination
Documented in Ordination
#'Ordination
#'@description
#'Plots Ordinations Plots.
#'@param data List of data generates by the Multivar function.
#'@param minimumRowsAfterCutOutMaxAge minimum rows count after filtering.
#'@param allspan The span for all loess functions.
#'@param MaxAge The max Age where to cut the Data.
#'@importFrom grDevices rainbow
#'@importFrom graphics lines points text legend barplot par axis
#'@importFrom stats na.omit
#'@importFrom grDevices dev.off pdf
#'@export
#'@return nothing.
#'@author Tim Kroeger
#'@note This function has only been developed for the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research and should therefore only be used in combination with their database.
Ordination = function(data, minimumRowsAfterCutOutMaxAge = 12, allspan = 1, MaxAge = 20000){
#new
orginalWorkingDirectoryPath=getwd()
getwdTry <- try(setwd(paste(getwd(),.Platform[2],paste("Out_",data$Filter,sep = ""),sep="")),silent = TRUE)
if(class(getwdTry) == "try-error"){
dir.create(paste(getwd(),.Platform[2],paste("Out_",data$Filter,sep = ""),sep=""))
setwd(paste(getwd(),.Platform[2],paste("Out_",data$Filter,sep = ""),sep=""))
}
CutOutMaxAge = function(DataVector, Cutage, minimumRowsAfterCutOutMaxAge){
FittingAges = as.numeric(row.names(DataVector))<=Cutage
if(sum(FittingAges)>minimumRowsAfterCutOutMaxAge){
DataVector=cbind(as.numeric(row.names(DataVector))[FittingAges],DataVector[FittingAges])
return(DataVector)
}else{
return(NULL)
}
}
CutOutMaxAgeForInterpolatedData = function(DataVector, Cutage, minimumRowsAfterCutOutMaxAge){
FittingAges = as.numeric(DataVector[,1])<=Cutage
if(sum(FittingAges)>minimumRowsAfterCutOutMaxAge){
DataVector=cbind(DataVector[FittingAges,1],DataVector[FittingAges,2])
return(DataVector)
}else{
return(NULL)
}
}
NormalizeRichnessDataTable = function(RichnessData){
for (i in 1:dim(RichnessData)[2]){
RichnessData[,i] = (RichnessData[,i]-min(RichnessData[,i]))/(max(RichnessData[,i])-min(RichnessData[,i]))
}
return(RichnessData)
}
AgeNormailzer = function(RichnessData){
RichnessData = RichnessData[,1]
return((RichnessData-min(RichnessData))/(max(RichnessData)-min(RichnessData)))
}
DeleteMeanNAS = function(MeanMatrix){
MeanMatrixCounter = 0
while (MeanMatrixCounter<dim(MeanMatrix)[1]) {
MeanMatrixCounter = MeanMatrixCounter+1
if(MeanMatrix[MeanMatrixCounter,2]==0){
MeanMatrix = MeanMatrix[-MeanMatrixCounter,]
MeanMatrixCounter = MeanMatrixCounter-1
}
}
return(MeanMatrix)
}
################################################################################
################################# Discription ##################################
################################################################################
Allcolor = rainbow(dim(data$CoreList)[1])
AllDiatomsNames = ls(data$Diatom)
AllCarbonsNames = ls(data$Carbon)
AllTRACENames = ls(data$TRACE)
################################################################################
##################################### MDS ######################################
################################################################################
PlotsVariantsLoess = c("Normal","Loess")
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
if(VariantsTransform == "Non Transformed"){
pdf(paste("Z_old MDS_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
#Plot Limits
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(rownames(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old MDS | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
if(VariantsTransform == "Transformed"){
pdf(paste("Z_old MDS_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(rownames(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old MDS | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$nMDS$Dim1)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
################################################################################
################################### Richness ###################################
################################################################################
nameLoess="Loess_invsimpson"
nameNormal="invsimpson"
minlegth = 10
PlotsVariantsLoess = c("Normal","Loess")
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
if(VariantsTransform == "Non Transformed"){
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
if(!is.null(Values)){
pdf(paste("Z_old N2_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
#Plot Limits
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]]) # $nMDS$Dim1
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
valueNames = as.numeric(rownames(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old N2 | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
}
dev.off()
}
}
if(VariantsTransform == "Transformed"){
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
if(!is.null(Values)){
pdf(paste("Z_old N2_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
valueNames = as.numeric(rownames(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old N2 | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Diatom[[DiatomsNames]]$Species_richness[[nameNormal]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
if(length(Values)>=minlegth){
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
}
dev.off()
}
}
}
}
################################################################################
##################################### Clima ####################################
################################################################################
PlotsVariantsLoess = c("Normal","Loess")
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
if(VariantsTransform == "Non Transformed"){
pdf(paste("PalioClima_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
#Plot Limits
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){ # length(data$Diatom)
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Palio Clima | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[1,1]+distance,
Values[1,2],
label=RID,
col="white",
cex=1.2)
text(Values[1,1]+distance,
Values[1,2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
if(VariantsTransform == "Transformed"){
pdf(paste("PalioClima_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Palio Clima | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Clima$annual[[DiatomsNames]])
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
valueNames = as.numeric(names(Values))
Values = as.numeric(Values)
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[1,1]+distance,
Values[1,2],
label=RID,
col="white",
cex=1.2)
text(Values[1,1]+distance,
Values[1,2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
################################################################################
#################################### Carbon ####################################
################################################################################
nameLoess="Loess_invsimpson"
nameNormal="invsimpson"
PlotsVariantsLoess = c("Normal","Loess")
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
if(VariantsTransform == "Non Transformed"){
pdf(paste("Z_old TOC_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
#Plot Limits
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC) # $Species_richness[[nameNormal]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old TOC | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){ #length(data$Diatom)
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
if(VariantsTransform == "Transformed"){
pdf(paste("Z_old TOC_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("Z_old TOC | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
points(Values[,1],Values[,2],col=Allcolor[RID], lwd=1, cex= 0.8)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = na.omit(data$Carbon[[DiatomsNames]]$rawData$TOC)
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(length(Values)>0){
Values = data$Carbon[[DiatomsNames]]$rawData
valueNames = Values$depth[!is.na(Values$TOC)]
Values = Values$TOC[!is.na(Values$TOC)]
#AgeError
Values = Values[!is.na(valueNames)]
valueNames = valueNames[!is.na(valueNames)]
Values = matrix(Values, ncol = 1)
rownames(Values) = valueNames
Values = CutOutMaxAge(Values,MaxAge,minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
Values[,2] = scale(Values[,2],center = T, scale = T)
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
#Legend
pdf("Legend.pdf",width=15,height=10)
# AllCoreList dim(data$CoreList)[1]
LegendsSplit = length(Allcolor)/3
SplitValue1 = ceiling(LegendsSplit)
SplitValue2 = ceiling(LegendsSplit*2)
SplitValue3 = length(Allcolor)
AllNamesPart1 = data$CoreList[,1][1:SplitValue1]
AllNamesPart2 = data$CoreList[,1][(SplitValue1+1):SplitValue2]
AllNamesPart3 = data$CoreList[,1][(SplitValue2+1):SplitValue3]
plot(NA,
ylim=c(0,1),
xlim=c(0,1),
ylab="",
xlab="",
main="Legend",
bty="n",
xaxt='n',
yaxt='n'
)
thickness = 1
legend(x = "left", legend = paste(1:SplitValue1,"-",AllNamesPart1), col = Allcolor[1:SplitValue1], lty = 1, lwd = 3, text.width=0.3, cex = thickness, bty="n") # bty="n"
legend(x = "center", legend = paste((SplitValue1+1):SplitValue2,"-",AllNamesPart2), col = Allcolor[(SplitValue1+1):SplitValue2], lty = 1, lwd = 3, text.width=0.3, cex = thickness, bty="n") # bty="n"
legend(x = "right", legend = paste((SplitValue2+1):SplitValue3,"-",AllNamesPart3), col = Allcolor[(SplitValue2+1):SplitValue3], lty = 1, lwd = 3, text.width=0.3, cex = thickness, bty="n") # bty="n"
dev.off()
################################################################################
###################################### RoC #####################################
################################################################################
pdf("RoC.pdf",width=15,height=10)
RocMatrix = data$RocMatrix
#Cut Data after x years
CutValue = MaxAge
RocMatrix=RocMatrix[RocMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
RocMatrix = DeleteMeanNAS(RocMatrix)
Xmin = min(RocMatrix[,1])
Xmax = max(RocMatrix[,1])
Ymin = min(RocMatrix[,3], na.rm = T)
Ymax = max(RocMatrix[,4], na.rm = T)
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(RocMatrix)[1])
P_color[] = "red"
P_color[RocMatrix[,5]<P_value] = "green"
P_color[RocMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("RoC Mean",sep="")
)
#lines(RocMatrix[,1],RocMatrix[,2], col= "blue")
#points(RocMatrix[,1],RocMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(RocMatrix)[1]-1)){
lines(RocMatrix[i:(i+1),1],RocMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(RocMatrix[,1],RocMatrix[,3], col= "black",lty=3,lwd=2)
lines(RocMatrix[,1],RocMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# RoC ##################################
############################### vector ################################
################################################################################
pdf("Vector_RoC.pdf",width=15,height=10)
RocMatrix = data$Vector_RocMatrix
#Cut Data after x years
CutValue = MaxAge
RocMatrix=RocMatrix[RocMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
RocMatrix = DeleteMeanNAS(RocMatrix)
Xmin = min(RocMatrix[,1])
Xmax = max(RocMatrix[,1])
Ymin = min(na.omit(RocMatrix[,3]))
Ymax = max(na.omit(RocMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(RocMatrix)[1])
P_color[] = "red"
P_color[RocMatrix[,5]<P_value] = "green"
P_color[RocMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Normal - RoC Mean",sep="")
)
for (i in 1:(dim(RocMatrix)[1]-1)){
lines(RocMatrix[i:(i+1),1],RocMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(RocMatrix[,1],RocMatrix[,3], col= "black",lty=3,lwd=2)
lines(RocMatrix[,1],RocMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# RoC ##################################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_RoC_Transform.pdf",width=15,height=10)
RocMatrix = data$Vector_RocMatrix_transform
#Cut Data after x years
CutValue = MaxAge
RocMatrix=RocMatrix[RocMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
RocMatrix = DeleteMeanNAS(RocMatrix)
Xmin = min(RocMatrix[,1])
Xmax = max(RocMatrix[,1])
Ymin = min(na.omit(RocMatrix[,3]))
Ymax = max(na.omit(RocMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(RocMatrix)[1])
P_color[] = "red"
P_color[RocMatrix[,5]<P_value] = "green"
P_color[RocMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - RoC Mean",sep="")
)
for (i in 1:(dim(RocMatrix)[1]-1)){
lines(RocMatrix[i:(i+1),1],RocMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(RocMatrix[,1],RocMatrix[,3], col= "black",lty=3,lwd=2)
lines(RocMatrix[,1],RocMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################### Evenness ###################################
################################################################################
pdf("Evenness.pdf",width=15,height=10)
EvennessMatrix = data$EvennessMatrix
#Cut Data after x years
CutValue = MaxAge
EvennessMatrix=EvennessMatrix[EvennessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
EvennessMatrix = DeleteMeanNAS(EvennessMatrix)
Xmin = min(EvennessMatrix[,1])
Xmax = max(EvennessMatrix[,1])
Ymin = min(EvennessMatrix[,3])
Ymax = max(EvennessMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(EvennessMatrix)[1])
P_color[] = "red"
P_color[EvennessMatrix[,5]<P_value] = "green"
P_color[EvennessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Evenness Mean",CutValue,sep="")
)
#lines(EvennessMatrix[,1],EvennessMatrix[,2], col= "blue")
#points(EvennessMatrix[,1],EvennessMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(EvennessMatrix)[1]-1)){
lines(EvennessMatrix[i:(i+1),1],EvennessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(EvennessMatrix[,1],EvennessMatrix[,3], col= "black",lty=3,lwd=2)
lines(EvennessMatrix[,1],EvennessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
############################## Evenness ################################
############################### vector ################################
################################################################################
pdf("Vector_Evenness.pdf",width=15,height=10)
EvennessMatrix = data$Vector_Evenness
#Cut Data after x years
CutValue = MaxAge
EvennessMatrix=EvennessMatrix[EvennessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
EvennessMatrix = DeleteMeanNAS(EvennessMatrix)
Xmin = min(EvennessMatrix[,1])
Xmax = max(EvennessMatrix[,1])
Ymin = min(na.omit(EvennessMatrix[,3]))
Ymax = max(na.omit(EvennessMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(EvennessMatrix)[1])
P_color[] = "red"
P_color[EvennessMatrix[,5]<P_value] = "green"
P_color[EvennessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - evenness Mean",sep="")
)
for (i in 1:(dim(EvennessMatrix)[1]-1)){
lines(EvennessMatrix[i:(i+1),1],EvennessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(EvennessMatrix[,1],EvennessMatrix[,3], col= "black",lty=3,lwd=2)
lines(EvennessMatrix[,1],EvennessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
############################## Evenness ################################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_Evenness_Transform.pdf",width=15,height=10)
EvennessMatrix = data$Vector_Evenness_transform
#Cut Data after x years
CutValue = MaxAge
EvennessMatrix=EvennessMatrix[EvennessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
EvennessMatrix = DeleteMeanNAS(EvennessMatrix)
Xmin = min(EvennessMatrix[,1])
Xmax = max(EvennessMatrix[,1])
Ymin = min(na.omit(EvennessMatrix[,3]))
Ymax = max(na.omit(EvennessMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(EvennessMatrix)[1])
P_color[] = "red"
P_color[EvennessMatrix[,5]<P_value] = "green"
P_color[EvennessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - evenness Mean",sep="")
)
for (i in 1:(dim(EvennessMatrix)[1]-1)){
lines(EvennessMatrix[i:(i+1),1],EvennessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(EvennessMatrix[,1],EvennessMatrix[,3], col= "black",lty=3,lwd=2)
lines(EvennessMatrix[,1],EvennessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
############################### InverseSimpsion ################################
################################################################################
pdf("InverseSimpsion.pdf",width=15,height=10)
InverseSimpsionMatrix = data$InverseSimpsionMatrix
#Cut Data after x years
CutValue = MaxAge
InverseSimpsionMatrix=InverseSimpsionMatrix[InverseSimpsionMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
InverseSimpsionMatrix = DeleteMeanNAS(InverseSimpsionMatrix)
Xmin = min(InverseSimpsionMatrix[,1])
Xmax = max(InverseSimpsionMatrix[,1])
Ymin = min(InverseSimpsionMatrix[,3])
Ymax = max(InverseSimpsionMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(InverseSimpsionMatrix)[1])
P_color[] = "red"
P_color[InverseSimpsionMatrix[,5]<P_value] = "green"
P_color[InverseSimpsionMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("InverseSimpsion Mean",CutValue,sep="")
)
#lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,2], col= "blue")
#points(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(InverseSimpsionMatrix)[1]-1)){
lines(InverseSimpsionMatrix[i:(i+1),1],InverseSimpsionMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,3], col= "black",lty=3,lwd=2)
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
########################## InverseSimpsion #############################
############################### vector ################################
################################################################################
pdf("Vector_InverseSimpsion.pdf",width=15,height=10)
InverseSimpsionMatrix = data$Vector_InverseSimpsion
#Cut Data after x years
CutValue = MaxAge
InverseSimpsionMatrix=InverseSimpsionMatrix[InverseSimpsionMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
InverseSimpsionMatrix = DeleteMeanNAS(InverseSimpsionMatrix)
Xmin = min(InverseSimpsionMatrix[,1])
Xmax = max(InverseSimpsionMatrix[,1])
Ymin = min(na.omit(InverseSimpsionMatrix[,3]))
Ymax = max(na.omit(InverseSimpsionMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(InverseSimpsionMatrix)[1])
P_color[] = "red"
P_color[InverseSimpsionMatrix[,5]<P_value] = "green"
P_color[InverseSimpsionMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - InverseSimpsion Mean",sep="")
)
for (i in 1:(dim(InverseSimpsionMatrix)[1]-1)){
lines(InverseSimpsionMatrix[i:(i+1),1],InverseSimpsionMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,3], col= "black",lty=3,lwd=2)
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
########################## InverseSimpsion #############################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_InverseSimpsion_Transform.pdf",width=15,height=10)
InverseSimpsionMatrix = data$Vector_InverseSimpsion_transform
#Cut Data after x years
CutValue = MaxAge
InverseSimpsionMatrix=InverseSimpsionMatrix[InverseSimpsionMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
InverseSimpsionMatrix = DeleteMeanNAS(InverseSimpsionMatrix)
Xmin = min(InverseSimpsionMatrix[,1])
Xmax = max(InverseSimpsionMatrix[,1])
Ymin = min(na.omit(InverseSimpsionMatrix[,3]))
Ymax = max(na.omit(InverseSimpsionMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(InverseSimpsionMatrix)[1])
P_color[] = "red"
P_color[InverseSimpsionMatrix[,5]<P_value] = "green"
P_color[InverseSimpsionMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - InverseSimpsion Mean",sep="")
)
for (i in 1:(dim(InverseSimpsionMatrix)[1]-1)){
lines(InverseSimpsionMatrix[i:(i+1),1],InverseSimpsionMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,3], col= "black",lty=3,lwd=2)
lines(InverseSimpsionMatrix[,1],InverseSimpsionMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
############################### SpeciesRichness ################################
################################################################################
pdf("SpeciesRichness.pdf",width=15,height=10)
SpeciesRichnessMatrix = data$SpeciesRichnessMatrix
#Cut Data after x years
CutValue = MaxAge
SpeciesRichnessMatrix=SpeciesRichnessMatrix[SpeciesRichnessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
SpeciesRichnessMatrix = DeleteMeanNAS(SpeciesRichnessMatrix)
Xmin = min(SpeciesRichnessMatrix[,1])
Xmax = max(SpeciesRichnessMatrix[,1])
Ymin = min(SpeciesRichnessMatrix[,3])
Ymax = max(SpeciesRichnessMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(SpeciesRichnessMatrix)[1])
P_color[] = "red"
P_color[SpeciesRichnessMatrix[,5]<P_value] = "green"
P_color[SpeciesRichnessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("SpeciesRichness Mean",CutValue,sep="")
)
for (i in 1:(dim(SpeciesRichnessMatrix)[1]-1)){
lines(SpeciesRichnessMatrix[i:(i+1),1],SpeciesRichnessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,3], col= "black",lty=3,lwd=2)
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
########################## SpeciesRichness #############################
############################### vector ################################
################################################################################
pdf("Vector_SpeciesRichness.pdf",width=15,height=10)
SpeciesRichnessMatrix = data$Vector_SpeciesRichness
#Cut Data after x years
CutValue = MaxAge
SpeciesRichnessMatrix=SpeciesRichnessMatrix[SpeciesRichnessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
SpeciesRichnessMatrix = DeleteMeanNAS(SpeciesRichnessMatrix)
Xmin = min(SpeciesRichnessMatrix[,1])
Xmax = max(SpeciesRichnessMatrix[,1])
Ymin = min(na.omit(SpeciesRichnessMatrix[,3]))
Ymax = max(na.omit(SpeciesRichnessMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(SpeciesRichnessMatrix)[1])
P_color[] = "red"
P_color[SpeciesRichnessMatrix[,5]<P_value] = "green"
P_color[SpeciesRichnessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - SpeciesRichness Mean",sep="")
)
for (i in 1:(dim(SpeciesRichnessMatrix)[1]-1)){
lines(SpeciesRichnessMatrix[i:(i+1),1],SpeciesRichnessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,3], col= "black",lty=3,lwd=2)
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
########################## SpeciesRichness #############################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_SpeciesRichness_Transform.pdf",width=15,height=10)
SpeciesRichnessMatrix = data$Vector_SpeciesRichness_transform
#Cut Data after x years
CutValue = MaxAge
SpeciesRichnessMatrix=SpeciesRichnessMatrix[SpeciesRichnessMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
SpeciesRichnessMatrix = DeleteMeanNAS(SpeciesRichnessMatrix)
Xmin = min(SpeciesRichnessMatrix[,1])
Xmax = max(SpeciesRichnessMatrix[,1])
Ymin = min(na.omit(SpeciesRichnessMatrix[,3]))
Ymax = max(na.omit(SpeciesRichnessMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(SpeciesRichnessMatrix)[1])
P_color[] = "red"
P_color[SpeciesRichnessMatrix[,5]<P_value] = "green"
P_color[SpeciesRichnessMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - SpeciesRichness Mean",sep="")
)
for (i in 1:(dim(SpeciesRichnessMatrix)[1]-1)){
lines(SpeciesRichnessMatrix[i:(i+1),1],SpeciesRichnessMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,3], col= "black",lty=3,lwd=2)
lines(SpeciesRichnessMatrix[,1],SpeciesRichnessMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
##################################### MDS ######################################
################################################################################
pdf("MDS.pdf",width=15,height=10)
MDSMatrix = data$MDSMatrix
#Cut Data after x years
CutValue = MaxAge
MDSMatrix=MDSMatrix[MDSMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
MDSMatrix = DeleteMeanNAS(MDSMatrix)
Xmin = min(MDSMatrix[,1])
Xmax = max(MDSMatrix[,1])
Ymin = min(MDSMatrix[,3])
Ymax = max(MDSMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(MDSMatrix)[1])
P_color[] = "red"
P_color[MDSMatrix[,5]<P_value] = "green"
P_color[MDSMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("MDS Mean",CutValue,sep="")
)
#lines(MDSMatrix[,1],MDSMatrix[,2], col= "blue")
#points(MDSMatrix[,1],MDSMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(MDSMatrix)[1]-1)){
lines(MDSMatrix[i:(i+1),1],MDSMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(MDSMatrix[,1],MDSMatrix[,3], col= "black",lty=3,lwd=2)
lines(MDSMatrix[,1],MDSMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################ MDS ###################################
############################### vector ################################
################################################################################
pdf("Vector_MDS.pdf",width=15,height=10)
MDSMatrix = data$Vector_MDS
#Cut Data after x years
CutValue = MaxAge
MDSMatrix=MDSMatrix[MDSMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
MDSMatrix = DeleteMeanNAS(MDSMatrix)
Xmin = min(MDSMatrix[,1])
Xmax = max(MDSMatrix[,1])
Ymin = min(na.omit(MDSMatrix[,3]))
Ymax = max(na.omit(MDSMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(MDSMatrix)[1])
P_color[] = "red"
P_color[MDSMatrix[,5]<P_value] = "green"
P_color[MDSMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - MDS Mean",sep="")
)
for (i in 1:(dim(MDSMatrix)[1]-1)){
lines(MDSMatrix[i:(i+1),1],MDSMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(MDSMatrix[,1],MDSMatrix[,3], col= "black",lty=3,lwd=2)
lines(MDSMatrix[,1],MDSMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# MDS ##################################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_MDS_Transform.pdf",width=15,height=10)
MDSMatrix = data$Vector_MDS_transform
#Cut Data after x years
CutValue = MaxAge
MDSMatrix=MDSMatrix[MDSMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
MDSMatrix = DeleteMeanNAS(MDSMatrix)
Xmin = min(MDSMatrix[,1])
Xmax = max(MDSMatrix[,1])
Ymin = min(na.omit(MDSMatrix[,3]))
Ymax = max(na.omit(MDSMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(MDSMatrix)[1])
P_color[] = "red"
P_color[MDSMatrix[,5]<P_value] = "green"
P_color[MDSMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - MDS Mean",sep="")
)
for (i in 1:(dim(MDSMatrix)[1]-1)){
lines(MDSMatrix[i:(i+1),1],MDSMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(MDSMatrix[,1],MDSMatrix[,3], col= "black",lty=3,lwd=2)
lines(MDSMatrix[,1],MDSMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
##################################### TOC ######################################
################################################################################
pdf("TOC.pdf",width=15,height=10)
TOCMatrix = data$TOCMatrix
#Cut Data after x years
CutValue = MaxAge
TOCMatrix=TOCMatrix[TOCMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
TOCMatrix = DeleteMeanNAS(TOCMatrix)
Xmin = min(TOCMatrix[,1])
Xmax = max(TOCMatrix[,1])
Ymin = min(TOCMatrix[,3])
Ymax = max(TOCMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(TOCMatrix)[1])
P_color[] = "red"
P_color[TOCMatrix[,5]<P_value] = "green"
P_color[TOCMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("TOC Mean",CutValue,sep="")
)
#lines(TOCMatrix[,1],TOCMatrix[,2], col= "blue")
#points(TOCMatrix[,1],TOCMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(TOCMatrix)[1]-1)){
lines(TOCMatrix[i:(i+1),1],TOCMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(TOCMatrix[,1],TOCMatrix[,3], col= "black",lty=3,lwd=2)
lines(TOCMatrix[,1],TOCMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# TOC ##################################
############################### vector ################################
################################################################################
pdf("Vector_TOC.pdf",width=15,height=10)
TOCMatrix = data$Vector_TOC
#Cut Data after x years
CutValue = MaxAge
TOCMatrix=TOCMatrix[TOCMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
TOCMatrix = DeleteMeanNAS(TOCMatrix)
Xmin = min(TOCMatrix[,1])
Xmax = max(TOCMatrix[,1])
Ymin = min(na.omit(TOCMatrix[,3]))
Ymax = max(na.omit(TOCMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(TOCMatrix)[1])
P_color[] = "red"
P_color[TOCMatrix[,5]<P_value] = "green"
P_color[TOCMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | normal - TOC Mean",sep="")
)
for (i in 1:(dim(TOCMatrix)[1]-1)){
lines(TOCMatrix[i:(i+1),1],TOCMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(TOCMatrix[,1],TOCMatrix[,3], col= "black",lty=3,lwd=2)
lines(TOCMatrix[,1],TOCMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
################################# TOC ##################################
############################### vector ################################
################################# transform ##################################
################################################################################
pdf("Vector_TOC_Transform.pdf",width=15,height=10)
TOCMatrix = data$Vector_TOC_transform
#Cut Data after x years
CutValue = MaxAge
TOCMatrix=TOCMatrix[TOCMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
TOCMatrix = DeleteMeanNAS(TOCMatrix)
Xmin = min(TOCMatrix[,1])
Xmax = max(TOCMatrix[,1])
Ymin = min(na.omit(TOCMatrix[,3]))
Ymax = max(na.omit(TOCMatrix[,4]))
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(TOCMatrix)[1])
P_color[] = "red"
P_color[TOCMatrix[,5]<P_value] = "green"
P_color[TOCMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("Vector | Transform - TOC Mean",sep="")
)
for (i in 1:(dim(TOCMatrix)[1]-1)){
lines(TOCMatrix[i:(i+1),1],TOCMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(TOCMatrix[,1],TOCMatrix[,3], col= "black",lty=3,lwd=2)
lines(TOCMatrix[,1],TOCMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
################################################################################
#################################### TRACE #####################################
################################################################################
TRACEMeanPlot= function(TraceName = "UnknownTrace"){
pdf(paste("TRACE_",TraceName,".pdf",sep=""),width=15,height=10)
TRACEMatrix = data$TRACEMatrix[[TraceName]]
#Cut Data after x years
CutValue = MaxAge
TRACEMatrix=TRACEMatrix[TRACEMatrix[,1]<=CutValue,]
#Plot Mean Rat of change
TRACEMatrix = DeleteMeanNAS(TRACEMatrix)
Xmin = min(TRACEMatrix[,1])
Xmax = max(TRACEMatrix[,1])
Ymin = min(TRACEMatrix[,3])
Ymax = max(TRACEMatrix[,4])
#Create Color after p Value from
P_value = 0.05
P_color <- vector( "character" , dim(TRACEMatrix)[1])
P_color[] = "red"
P_color[TRACEMatrix[,5]<P_value] = "green"
P_color[TRACEMatrix[,6]==1] = "black"
plot(NA,
ylim=c(Ymin,Ymax),
xlim=c(Xmax,Xmin),
ylab="Value",
xlab="Age",
main=paste("TRACE Mean ",TraceName," ",CutValue,sep="")
)
#lines(TRACEMatrix[,1],TRACEMatrix[,2], col= "blue")
#points(TRACEMatrix[,1],TRACEMatrix[,2], pch = 19, cex = 0.1, col= "black")
for (i in 1:(dim(TRACEMatrix)[1]-1)){
lines(TRACEMatrix[i:(i+1),1],TRACEMatrix[i:(i+1),2], col= P_color[i],lwd=2)
}
lines(TRACEMatrix[,1],TRACEMatrix[,3], col= "black",lty=3,lwd=2)
lines(TRACEMatrix[,1],TRACEMatrix[,4], col= "black",lty=3,lwd=2)
dev.off()
}
TRACEMeanPlot(TraceName = "JJA_mean")
TRACEMeanPlot(TraceName = "DJF_mean")
TRACEMeanPlot(TraceName = "hydrological_mean")
################################################################################
################################ Evenness Solo #################################
################################################################################
ImportVersions = c("evenness","Cut_evenness")
for (IV in ImportVersions){
PlotsVariantsLoess = c("Normal","Loess") # "Normal","Loess"
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
################################################################################
################################### RoC Solo ###################################
################################################################################
ImportVersions = c("RoC","Cut_RoC")
for (IV in ImportVersions){
PlotsVariantsLoess = c("Normal","Loess") # "Normal","Loess"
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsLoess in PlotsVariantsLoess){
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(VariantsLoess == "Loess"){
#Loess
ValuesLoess=loess(Values[,2] ~ Values[,1], span=allspan)
Values = cbind(Values[,1],ValuesLoess$fitted)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
}
##############################################################################
############################## Correction Output #############################
##############################################################################
CorrectionPlots(data = data, AllDiatomsNames = AllDiatomsNames, MaxAge = MaxAge)
##############################################################################
############################ Inverse Simpson Solo ############################
##############################################################################
ImportVersions = c("inverseSimpsionData","Cut_inverseSimpsionData")
for (IV in ImportVersions){
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
PointValues = data$Diatom[[DiatomsNames]]$Species_richness$invsimpson
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
points(as.numeric(row.names(PointValues)),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
##############################################################################
########################### Species Richness Solo ############################
##############################################################################
ImportVersions = c("speciesRichnessData","Cut_speciesRichnessData")
for (IV in ImportVersions){
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
PointValues = data$Diatom[[DiatomsNames]]$Species_richness$richness
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
points(as.numeric(row.names(PointValues)),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
##############################################################################
################################## MDS Solo ##################################
##############################################################################
ImportVersions = c("MDS","Cut_MDS")
for (IV in ImportVersions){
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste(IV,"_",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste(IV," | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
PointValues = data$Diatom[[DiatomsNames]]$nMDS$Dim1
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
points(as.numeric(row.names(PointValues)),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
Values = data$Diatom[[DiatomsNames]][[IV]]
RID = which(data$CoreList[,1]==DiatomsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values, MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
}
##############################################################################
################################## TOC Solo ##################################
##############################################################################
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste("TOC__",VariantsLoess, "_",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$Carbon)){
CarbonsNames = AllCarbonsNames[i]
Values = data$Carbon[[CarbonsNames]]$TOC
RID = which(data$CoreList[,1]==CarbonsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("TOC | ",VariantsLoess, " | ",VariantsTransform,sep="")
)
for (i in 1:length(data$Carbon)){
CarbonsNames = AllCarbonsNames[i]
Values = data$Carbon[[CarbonsNames]]$TOC
RID = which(data$CoreList[,1]==CarbonsNames)
PointValues = data$Carbon[[CarbonsNames]]$rawData$TOC
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
points(as.numeric(data$Carbon[[CarbonsNames]]$rawData$depth),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
#Plot Numbers
for (i in 1:length(data$Carbon)){
CarbonsNames = AllCarbonsNames[i]
Values = data$Carbon[[CarbonsNames]]$TOC
RID = which(data$CoreList[,1]==CarbonsNames)
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values, MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
dev.off()
}
##############################################################################
################################# TRACE Solo #################################
##############################################################################
TRACESoloPlot= function(TraceType = "UnknownTrace"){
PlotsVariantsTransform = c("Non Transformed","Transformed")
for (VariantsTransform in PlotsVariantsTransform){
Xmax=0
Ymax=0
Xmin=Inf
Ymin=Inf
pdf(paste("TRACE_",TraceType," ",VariantsTransform,".pdf",sep=""),width=15,height=10)
for (i in 1:length(data$TRACE)){
TRACEsNames = AllTRACENames[i]
Values = data$TRACE[[TRACEsNames]][[TraceType]]
RID = which(data$CoreList[,1]==TRACEsNames)
if(length(RID)>0){
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
if(max(Values[,2])>Xmax){
Xmax=max(Values[,2])
}
if(min(Values[,2])<Xmin){
Xmin=min(Values[,2])
}
if(max(Values[,1])>Ymax){
Ymax=max(Values[,1])
}
if(min(Values[,1])<Ymin){
Ymin=min(Values[,1])
}
}
}
}
}
}
plot(NA,
ylim=c(Xmin,Xmax),
xlim=c(Ymax,Ymin),
ylab="Value",
xlab="Age",
main=paste("TRACE | ",TraceType," ",VariantsTransform,sep="")
)
for (i in 1:length(data$TRACE)){
TRACEsNames = AllTRACENames[i]
Values = data$TRACE[[TRACEsNames]][[TraceType]]
RID = which(data$CoreList[,1]==TRACEsNames)
#PointValues = data$TRACE[[TRACEsNames]]$rawData$TRACE
if(length(RID)>0){
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values,MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
#PointValues = scale(PointValues,center = T, scale = T)
}
lines(Values[,1],Values[,2],col=Allcolor[RID], lwd=1)
#points(as.numeric(data$TRACE[[TRACEsNames]]$rawData$depth),PointValues,col=Allcolor[RID], lwd=1, cex= 0.8)
}
}
}
}
}
#Plot Numbers
for (i in 1:length(data$TRACE)){
TRACEsNames = AllTRACENames[i]
Values = data$TRACE[[TRACEsNames]][[TraceType]]
RID = which(data$CoreList[,1]==TRACEsNames)
if(length(RID)>0){
if(!is.null(Values)){
if(dim(Values)[1]>0){
Values = CutOutMaxAgeForInterpolatedData(Values, MaxAge, minimumRowsAfterCutOutMaxAge)
if(!is.null(Values)){
if(VariantsTransform=="Transformed"){
Values[,2] = scale(Values[,2],center = T, scale = T)
}
distance = 120
if(i>=10){distance = 200}
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col="white",
cex=1.2)
text(Values[dim(Values)[1],1]+distance,
Values[dim(Values)[1],2],
label=RID,
col=Allcolor[RID])
}
}
}
}
}
dev.off()
}
}
TRACESoloPlot(TraceType = "JJA_mean")
TRACESoloPlot(TraceType = "DJF_mean")
TRACESoloPlot(TraceType = "hydrological_mean")
################################################################################
################################# StressPlot ###################################
################################################################################
pdf("Stress.pdf",width=15,height=10)
DiatomNames = ls(data$Diatom)
LakeType = vector("character",length(DiatomNames))
Stress = vector("numeric",length(DiatomNames))
Names = DiatomNames
counter = 0
for (i in 1:length(DiatomNames)){
counter = counter+1
if (!sum(data$LakeData$CoreID==DiatomNames[counter])==0){
StressValue = data$Diatom[[DiatomNames[counter]]]$nMDS$Stress
if(!is.null(StressValue)){
LakeType[counter] = data$LakeDat$LakeType[which(data$LakeData$CoreID==DiatomNames[counter])]
Stress[counter] = StressValue
}else{
LakeType = LakeType[-counter]
Stress = Stress[-counter]
Names = Names[-counter]
DiatomNames = DiatomNames[-counter]
counter=counter-1
}
}else{
LakeType = LakeType[-counter]
Stress = Stress[-counter]
Names = Names[-counter]
DiatomNames = DiatomNames[-counter]
counter=counter-1
}
}
names(Stress) = Names
names(LakeType) = Names
Stress = Stress[!LakeType == "-"]
LakeType = LakeType[!LakeType == "-"]
sortStress = Stress[order(Stress,decreasing = F)]
sortLakeType = LakeType[order(Stress,decreasing = F)]
LakeFactor = as.factor(LakeType)
color = rainbow(length(levels(LakeFactor)))
colorVector <- vector( "character" , length(LakeType)[1])
for (c in 1:length(levels(LakeFactor))){
colorVector[which(LakeFactor == levels(LakeFactor)[c])] = color[c]
}
par(mar = c(5, 9, 5, 5))
par(mfrow = c(1,1))
barplot(sortStress,horiz = TRUE, las = 1, col = colorVector, main = "Stress Plot",
border = colorVector,
space=0.3)
legend(x = "bottom",
legend = levels(LakeFactor),
fill =color,
cex = 1.3,
bty = 'n',
inset = c(-0.15,0))
par(mar = c(5, 5, 5, 5))
par(mfrow = c(1,1))
dev.off()
#Distribution of measurements
#Diatom
pdf(paste("Diatom_DistributionOfMeasurements",".pdf",sep=""),width=15,height=15)
par(mar = c(5, 9, 5, 5))
par(mfrow = c(1,1))
plot(NA,
ylim=c(0,length(data$Diatom)),
xlim=c(20000,0),
ylab="",
xlab="Age",
main="Diatom | Distribution of measurements",
yaxt = "n"
)
axis(2, at = seq(0,length(data$Diatom)-1),
labels = AllDiatomsNames, las=2)
par(mar = c(5, 5, 5, 5))
par(mfrow = c(1,1))
for (i in 1:length(data$Diatom)){
DiatomsNames = AllDiatomsNames[i]
RawData = data$Diatom[[DiatomsNames]]$rawData
Occurence = RawData[,1]
Occurence = Occurence[Occurence <= 20000]
points(Occurence,seq((i-1), (i-1), length.out=length(Occurence)),col="black", lwd=1,pch = 20)
}
dev.off()
#Carbon
pdf(paste("Carbon_DistributionOfMeasurements",".pdf",sep=""),width=15,height=15)
par(mar = c(5, 9, 5, 5))
par(mfrow = c(1,1))
plot(NA,
ylim=c(0,length(data$Carbon)),
xlim=c(20000,0),
ylab="",
xlab="Age",
main="Carbon | Distribution of measurement",
yaxt = "n"
)
axis(2, at = seq(0,length(data$Carbon)-1),
labels = AllCarbonsNames, las=2)
par(mar = c(5, 5, 5, 5))
par(mfrow = c(1,1))
for (i in 1:length(data$Carbon)){
DiatomsNames = AllCarbonsNames[i]
RawData = data$Carbon[[DiatomsNames]]$rawData
RawData = RawData[!is.na(RawData$TOC),]
Occurence = RawData[,1]
Occurence = Occurence[Occurence <= 20000]
points(Occurence,seq((i-1), (i-1), length.out=length(Occurence)),col="black", lwd=1,pch = 20)
}
dev.off()
setwd(orginalWorkingDirectoryPath)
}
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