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# Functions to format data to be plotted with STdiag
# convert data from matrix to data frame.
# if values of x and y are not provided, the functions suppose
# x=seq(1,dim(mat)[1]) et
# y=seq(1,dim(mat)[2])
Matrix2DataFrame=function(mat,x=NULL,y=NULL,xlab="x",ylab="y",zlab="z")
{
# check if x and y are difined, if not, gives default values
if(is.null(y)){
y=seq(0,dim(mat)[2]-1)
}
if(is.null(x)){
x=seq(0,dim(mat)[1]-1)
}
# if dimensions do not match, return error message
dim=c(length(x),length(y))
if(sum(dim==dim(mat))!=2)
{
cat("ERROR: mat must be of dimension x,y.")
return(invisible())
}
# create output data frame
df=expand.grid(x,y)
# add z column to output data frame
df$z=c(mat)
# name columns of output data frame and return
colnames(df)=c(xlab,ylab,zlab)
return(df)
}
# Import individual based data set: in case data set is composed of one line
# per individual, with time and structure in columns.
Indiv2DataFrame=function(tab,classes=50,columns=c(1,2))
{
# define output data frame
DF=NULL
names=names(tab[,columns])
# get column number of x-axis variable and y-axis variable
x=columns[1]
y=columns[2]
# test if x is a date
is.date = inherits(tab[,x],"Date")
# define classes for discretization of y axis variable in classes classes
if(length(classes)==1){
ymin=min(tab[,y],na.rm=TRUE)
ymax=max(tab[,y],na.rm=TRUE)
yat=seq(ymin,ymax,length.out=classes+1)
} else if (length(classes)>1){
if(min(tab[,y])<min(classes) | max(tab[,y])>max(classes)){
cat("ERROR: 'classes' must span range of 'y'")
return()
}
yat=classes
}
# discretize y-axis variable in classes classes
for(t in unique(tab[,x]))
{
subtab=subset(tab,tab[,x]==t)
h=hist(subtab[,y],breaks=yat,plot=F)
DF=rbind(DF,x=data.frame(t,y=h$mids,counts=h$counts))
}
colnames(DF)=c(names,"counts")
if(is.date){
DF[,1] = as.Date(DF[,1],origin="1970-01-01")
}
# return result data frame
return(DF)
}
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