# ________________________________________________________
# ( 1 )

fct.plotL <- function ( Kestim, col=c(1,2), lty=c(1,1), ylab="L function", add=F, ylim=NULL){
	require(spatstat)
	if (!is.null(ylim)) { 
		plot ( Kestim, cbind(r=r-r, Lfunc=sqrt(trans/pi)-r)~r, col=0, lty=0, ylab=ylab, ylim=ylim)
		add <- T
	}
	if (!add) plot ( Kestim, cbind(r=r-r, Lfunc=sqrt(trans/pi)-r)~r, col=col, lty=lty, ylab=ylab)
	if (add) plot ( Kestim, cbind(r=r-r, Lfunc=sqrt(trans/pi)-r)~r, col=col, lty=lty, ylab=ylab, add=add )

# Represente le graph de la fonction L a partir d'un résultat K
#  -------------
# Kestim	= Estimation K (spatstat) a representer sous forme L
# col		= Couleurs Ref + Est Kcr
# lty		= Type ligne Ref + Est Kcr
# ylim		= Limites axe Y
# add		= Ajoute la courbe sur un graph existant
}

# ________________________________________________________
# ( 2 )

fct.KcrRandLab <- function (pppMrk, M1, M2, s, nsim=10, quiet=FALSE ) {
	require(spatstat)
	if (!quiet) cat("Doing randomization ", 1, "/",nsim,"\n")
	Mat <- Kcross ( pppMrk%mark%sample(pppMrk$marks), M1, M2, r=s, correction= "translate" )$trans
	for (isim in (2:nsim)) {
		if (!quiet) cat("Doing randomization ", isim, "/",nsim,"\n")
		Tmp <- Kcross ( pppMrk%mark%sample(pppMrk$marks), M1, M2, r=s, correction= "translate" )$trans
		Mat <- rbind(Mat,Tmp)  }

# Calcul matrice de randomization Kcross par Random Labelling
#  -------------
# pppMrk	= A marked point pattern
# M1 M2		= Marks
# s		= Distance lags
# Proba		= The proba for the confidence interval
# nsim		= Number of simulations / randomizations
}


# ________________________________________________________
# ( 3 )

fct.plot.ciL <- function ( Mat, s, Proba=0.05, lty=2, col=1, lwd=1 ) {
	kmin	<- apply(Mat, 2, quantile, probs=Proba/2, na.rm=T )
	kmax	<- apply(Mat, 2, quantile, probs=1-(Proba/2), na.rm=T )
	lines ( s, sqrt(kmin/pi)-s, lty=lty, col=col, lwd=lwd )
	lines ( s, sqrt(kmax/pi)-s, lty=lty, col=col, lwd=lwd )

# Represente l'interval de confiance sur graph fct.plotL
#  -------------
# Mat		= Matrice randomization calculée par fct.KcrRandLab
# s		= Distance lags
# Proba		= Proba de l'interval de confiance
# lty		= Type ligne	
# col		= Couleur
# lwd		= Epaisseur de la ligne
}