R/Code_package_ChooseRef.R
In RenaudJau/ChooseRef: Functions for the Durbecq et al. paper (in prep) about ecosystem references
Defines functions
Diss_Ref_Plot
Documented in
Diss_Ref_Plot
########### Functions of ChooseRef package ######################
#' Similarities between references and restoration
#'
#' @description Calculate similarity between plots and a group of reference plot
#'
#' @param RELEVES Variables of the restoration sites data matrix
#' @param REF Variables surveys of reference sites data matrix
#' @param METHOD Dissimilarity index, partial match to "manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao" or "mahalanobis". Cf \code{\link{vegan::vegdist}} function
#' @param BINARY (FALSE by default) If TRUE, data is converted to binary data
#' @param DUPLICATES (TRUE by default) Are REF plots also included in RELEVES data?
#'
#' @return \item{Diss_Mean}{average dissimilarity by RELEVES}
#' @return \item{Diss_Min}{minimal dissimilarity by RELEVES}
#' @return \item{RelRef_order}{list of REF names in ascending order of dissimilarity for each RELEVES}
#' @return \item{DistRef_order}{list of REF distance values in ascending order of dissimilarity for each RELEVES}
#'
#' @export
#'
#' @examples # ------------ Creating the data needed for the example --------------------
#' library(vegan)
#' data("dune") #downloading of dune data (cf vegan)
#' data("dune.env") #downloading of dune.env data (cf vegan)
#' # keeping only the numeric variables :
#' dune.env <- data.frame(A1 = dune.env$A1,
#' Moisture = as.numeric(as.vector(dune.env$Moisture)),
#' Manure = as.numeric(as.vector(dune.env$Manure)))
#'
#' # Creating a vector indicating which plots are the potential references and which ones are the restored sites
#' sites <- factor(c(rep("Rest",5),rep("Ref",15)))
#' # Creating a vector with the plot names
#' sites_names <- paste(sites,c(1:5,1:15))
#'
#' #Poviding names to rows (useful for the outputs)
#' row.names(dune.env) <- sites_names
#' row.names(dune) <- sites_names
#' dune.envRest <- dune.env[sites=="Rest",]
#' dune.envRef <- dune.env[sites=="Ref",]
#'
#' # -------------- Calculating reference dissimilarities ---------------------
#' Distances <- DissRef3(RELEVES = dune.envRest, REF = dune.envRef, METHOD = "euclidean", DUPLICATES = FALSE)
#' Distances
#'
#' # ---------------- Plotting reference dissimilarities ----------------------
#' Diss_Ref_Plot(RELEVES = dune.envRest, REF = dune.envRef, DISTANCES = Distances, LINK_NUMBER = "N_REF", N_REF = 3)
DissRef3 <- function (RELEVES, REF, METHOD = "bray", BINARY = FALSE, DUPLICATES = TRUE)
{
# Fusion of tables:
if(all.equal(names(RELEVES),names(REF))!=TRUE){warning("The two tables do not have the same variables")}
Tableaux <- rbind(RELEVES,REF)
# Dissimilarity matrix:
matrice <- as.matrix(vegdist(Tableaux, method = METHOD, binary = BINARY))
# Creation of variables:
Diss_Mean <- NULL # average dissimilarity of RELEVES
Diss_Min <- NULL # minimal dissimilarity of RELEVES
# Rel_order will be, for each RELEVES, the list of the names of REF in ascending order of dissimilarity
Rel_order <- data.frame(matrix(data = 0, nrow = nrow(RELEVES), ncol = nrow(REF)))
row.names(Rel_order) <- row.names(RELEVES) # line names = RELEVES names
# ‘Dist_order’: for each RELEVES, the list of distance values to REF in ascending order of dissimilarity
# The object is identical to ‘Rel_order’
Dist_order <- Rel_order
# loop to calculate for each relevé
for(i in 1:nrow(RELEVES))
{
# Ref distances (from the distance matrix)
diss_qn <- matrice[i,c((nrow(RELEVES)+1):(nrow(RELEVES)+nrow(REF)))]
# ‘if’ & ‘else’: to delete a zero in the distance matrix when there is a duplicate of data in RELEVES and in REF (in this case set DUPLICATES = TRUE) and we have a value 0 between the RELEVES i and at least one of REF
if(DUPLICATES==TRUE & min(diss_qn)==0)
{
diss_qnw0_2 <- diss_qn[-c(1:length(diss_qn))[diss_qn==0][1]]
} else {
diss_qnw0_2 <- diss_qn
}
# Placing the REF names in ascending order according to the distance to the RELEVES i
Rel_order[i,] <- factor(row.names(REF)[order(diss_qn)])
# Placing the distance values in ascending order according to the distance to RELEVES i
Dist_order[i,] <- diss_qn[order(diss_qn)]
# Average distance value
Diss_Mean[i] <- mean(diss_qnw0_2)
# Minimum distance value
Diss_Min[i] <- min(diss_qnw0_2)
}
Output <- list(Diss_Mean,Diss_Min,Rel_order,Dist_order)
names(Output) <- c("Diss_Mean","Diss_Min","RelRef_order","DistRef_order")
return(Output)
}
#' Plot of references and restoration site dissimilarities
#' @description Display similarity link (previously calculated with \code{\link{ChosseRef::DissRef3}} function) between plots and a group of reference plot
#'
#' @param RELEVES Variables of the restoration sites data matrix
#' @param REF Variables of reference sites data matrix
#' @param DISTANCES Object resulting from the analysis of the \code{\link{ChosseRef::DissRef3}} function.
#' @param METHOD Dissimilarity index, partial match to "manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao" or "mahalanobis". Cf \code{\link{vegan::vegdist}} function
#' @param COUL_RELEVES Color of RELEVES names
#' @param COUL_Rel_variable If TRUE: color change for each RELEVES
#' @param COUL_REF Color of REF names
#' @param COUL_Seg Color of links
#' @param COUL_Seg_variable If TRUE: colors change for each link
#' @param LINK_NUMBER Number of link drawn. If "N_REF" then for each RELEVES, the number of link drawn is the number given in \code{N_REF}. If "DIST_MIN"
#' only reference that have distance to restoration sites lower than the value given in \code{DIST_MIN} are drawn.
#' @param N_REF Number of REF to display a link between REF and RELEVES
#' @param DIST_MIN Minimum distance value to draw a link between REF and RELEVES
#' @param VAL_DIST Providing or not (TRUE by default) the distance value display
#' @param DECAL Distance between the printing of the distance and the link
#'
#' @export
#'
#' @examples # ------------ Creating the data needed for the example --------------------
#' library(vegan)
#' data("dune") #downloading of dune data (cf vegan)
#' data("dune.env") #downloading of dune.env data (cf vegan)
#' # keeping only the numeric variables :
#' dune.env <- data.frame(A1 = dune.env$A1,
#' Moisture = as.numeric(as.vector(dune.env$Moisture)),
#' Manure = as.numeric(as.vector(dune.env$Manure)))
#'
#' # Creating a vector indicating which plots are the potential references and which ones are the restored sites
#' sites <- factor(c(rep("Rest",5),rep("Ref",15)))
#' # Creating a vector with the plot names
#' sites_names <- paste(sites,c(1:5,1:15))
#'
#' #Poviding names to rows (useful for the outputs)
#' row.names(dune.env) <- sites_names
#' row.names(dune) <- sites_names
#' dune.envRest <- dune.env[sites=="Rest",]
#' dune.envRef <- dune.env[sites=="Ref",]
#'
#' # -------------- Calculating reference dissimilarities ---------------------
#' Distances <- DissRef3(RELEVES = dune.envRest, REF = dune.envRef, METHOD = "euclidean", DUPLICATES = FALSE)
#' Distances
#'
#' # ---------------- Plotting reference dissimilarities ----------------------
#' Diss_Ref_Plot(RELEVES = dune.envRest, REF = dune.envRef, DISTANCES = Distances, LINK_NUMBER = "N_REF", N_REF = 3)
Diss_Ref_Plot <- function(RELEVES, REF, DISTANCES,
METHOD = "euclidean",
COUL_RELEVES = 2, COUL_Rel_variable = TRUE,
COUL_REF = 1,
COUL_Seg = "#9C8809", COUL_Seg_variable = TRUE,
LINK_NUMBER="absent", N_REF="absent", DIST_MIN="absent",
VAL_DIST = TRUE, DECAL = 0 )
{
# Check that the names of RELEVES and REF are the same:
if(all.equal(names(RELEVES),
names(REF))!=TRUE){warning("REF and RELEVES don't have the same variables")}
# Verification that the information of CHOICE NUMBER, N_REF and DIST_MIN coincide well:
if(LINK_NUMBER!="N_REF" & LINK_NUMBER!="DIST_MIN"){warning("LINK_NUMBER is not correctly assigned")}
if(LINK_NUMBER=="N_REF" & N_REF=="absent"){warning("N_REF is not correctly assigned")}
if(LINK_NUMBER=="DIST_MIN" & DIST_MIN=="absent"){warning("DIST_MIN is not correctly assigned")}
# Verification that DIST_MIN is greater than the minimum distance for each RELEVES:
if(max(DISTANCES$DistRef_order[,1])>DIST_MIN){warning("DIST_MIN is too low and there is no reference close enough for each RELEVES")}
# Fusion of tables
Tableaux <- rbind(RELEVES,REF)
# NMDS analysis:
NMDS <- metaMDS(Tableaux, distance = METHOD, trace = FALSE)
# Display samples of RELEVES and REF
plot(NMDS$points, type="n", main="Choices of reference
with environmental conditions")
text(NMDS$points, labels = row.names(Tableaux),
col = c(rep(COUL_RELEVES, nrow(RELEVES)), rep(COUL_REF, nrow(REF))))
# Creation of the color palette if COUL_Seg_variable = TRUE:
if(COUL_Seg_variable==TRUE){
COUL_Seg <- colorRampPalette(colors=c("#0789e0","#9C093F","#9C0972","#91099C","#57099C","#26099C","#09199C","#09459C","#09819C","#099C8C","#099C5C","#099C26","#199C09","#4A9C09","#7B9C09","#9C9309","#9C6209","#9C3109","#9C0909"))(nrow(DISTANCES$RelRef_order))}else{COUL_Seg <- rep(COUL_Seg,nrow(DISTANCES$RelRef_order))
}
# Loop for each RELEVES:
for (j in 1:nrow(RELEVES))
{
# Coordinates of relevé:
Coo_REL <- NMDS$points[j,c(1:2)]
# If we choose to present a number of references:
if(LINK_NUMBER=="N_REF"){
for(i in 1:N_REF)
{
Num_ref_proche <- c(1:nrow(REF))[row.names(REF)==DISTANCES$RelRef_order[j,i]]
Coo_REF <- NMDS$points[nrow(RELEVES)+Num_ref_proche,c(1:2)]
segments(x0 = Coo_REL[1], y0 = Coo_REL[2], x1 = Coo_REF[1],
y1 = Coo_REF[2], col = COUL_Seg[j])
Dist_REF <- round(DISTANCES$DistRef_order[j,i],2)
if(VAL_DIST==TRUE){text(x = mean(c(Coo_REF[1],Coo_REL[1]))+DECAL,
y = mean(c(Coo_REF[2],Coo_REL[2]))+DECAL,
Dist_REF, col="Grey40", cex = 0.7)}
}
}
# If you choose a distance value for which to display the links:
if(LINK_NUMBER=="DIST_MIN"){
# Number of REF with distance less than DIST_MIN:
N_REF <- length(which(DISTANCES$DistRef_order[1,]<DIST_MIN))
for(i in 1:N_REF)
{
Num_ref_proche <- c(1:nrow(REF))[row.names(REF)==DISTANCES$RelRef_order[j,i]]
Coo_REF <- NMDS$points[nrow(RELEVES)+Num_ref_proche,c(1:2)]
segments(x0 = Coo_REL[1], y0 = Coo_REL[2], x1 = Coo_REF[1],
y1 = Coo_REF[2], col = COUL_Seg[j])
Dist_REF <- round(DISTANCES$DistRef_order[j,i],2)
if(VAL_DIST==TRUE){text(x = mean(c(Coo_REF[1],Coo_REL[1]))+DECAL,
y = mean(c(Coo_REF[2],Coo_REL[2]))+DECAL,
Dist_REF, col="Grey40", cex = 0.7)}
}
}
}
}
RenaudJau/ChooseRef documentation built on April 20, 2020, 10:45 p.m.
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Defines functions Diss_Ref_Plot
Documented in Diss_Ref_Plot
########### Functions of ChooseRef package ######################
#' Similarities between references and restoration
#'
#' @description Calculate similarity between plots and a group of reference plot
#'
#' @param RELEVES Variables of the restoration sites data matrix
#' @param REF Variables surveys of reference sites data matrix
#' @param METHOD Dissimilarity index, partial match to "manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao" or "mahalanobis". Cf \code{\link{vegan::vegdist}} function
#' @param BINARY (FALSE by default) If TRUE, data is converted to binary data
#' @param DUPLICATES (TRUE by default) Are REF plots also included in RELEVES data?
#'
#' @return \item{Diss_Mean}{average dissimilarity by RELEVES}
#' @return \item{Diss_Min}{minimal dissimilarity by RELEVES}
#' @return \item{RelRef_order}{list of REF names in ascending order of dissimilarity for each RELEVES}
#' @return \item{DistRef_order}{list of REF distance values in ascending order of dissimilarity for each RELEVES}
#'
#' @export
#'
#' @examples # ------------ Creating the data needed for the example --------------------
#' library(vegan)
#' data("dune") #downloading of dune data (cf vegan)
#' data("dune.env") #downloading of dune.env data (cf vegan)
#' # keeping only the numeric variables :
#' dune.env <- data.frame(A1 = dune.env$A1,
#' Moisture = as.numeric(as.vector(dune.env$Moisture)),
#' Manure = as.numeric(as.vector(dune.env$Manure)))
#'
#' # Creating a vector indicating which plots are the potential references and which ones are the restored sites
#' sites <- factor(c(rep("Rest",5),rep("Ref",15)))
#' # Creating a vector with the plot names
#' sites_names <- paste(sites,c(1:5,1:15))
#'
#' #Poviding names to rows (useful for the outputs)
#' row.names(dune.env) <- sites_names
#' row.names(dune) <- sites_names
#' dune.envRest <- dune.env[sites=="Rest",]
#' dune.envRef <- dune.env[sites=="Ref",]
#'
#' # -------------- Calculating reference dissimilarities ---------------------
#' Distances <- DissRef3(RELEVES = dune.envRest, REF = dune.envRef, METHOD = "euclidean", DUPLICATES = FALSE)
#' Distances
#'
#' # ---------------- Plotting reference dissimilarities ----------------------
#' Diss_Ref_Plot(RELEVES = dune.envRest, REF = dune.envRef, DISTANCES = Distances, LINK_NUMBER = "N_REF", N_REF = 3)
DissRef3 <- function (RELEVES, REF, METHOD = "bray", BINARY = FALSE, DUPLICATES = TRUE)
{
# Fusion of tables:
if(all.equal(names(RELEVES),names(REF))!=TRUE){warning("The two tables do not have the same variables")}
Tableaux <- rbind(RELEVES,REF)
# Dissimilarity matrix:
matrice <- as.matrix(vegdist(Tableaux, method = METHOD, binary = BINARY))
# Creation of variables:
Diss_Mean <- NULL # average dissimilarity of RELEVES
Diss_Min <- NULL # minimal dissimilarity of RELEVES
# Rel_order will be, for each RELEVES, the list of the names of REF in ascending order of dissimilarity
Rel_order <- data.frame(matrix(data = 0, nrow = nrow(RELEVES), ncol = nrow(REF)))
row.names(Rel_order) <- row.names(RELEVES) # line names = RELEVES names
# ‘Dist_order’: for each RELEVES, the list of distance values to REF in ascending order of dissimilarity
# The object is identical to ‘Rel_order’
Dist_order <- Rel_order
# loop to calculate for each relevé
for(i in 1:nrow(RELEVES))
{
# Ref distances (from the distance matrix)
diss_qn <- matrice[i,c((nrow(RELEVES)+1):(nrow(RELEVES)+nrow(REF)))]
# ‘if’ & ‘else’: to delete a zero in the distance matrix when there is a duplicate of data in RELEVES and in REF (in this case set DUPLICATES = TRUE) and we have a value 0 between the RELEVES i and at least one of REF
if(DUPLICATES==TRUE & min(diss_qn)==0)
{
diss_qnw0_2 <- diss_qn[-c(1:length(diss_qn))[diss_qn==0][1]]
} else {
diss_qnw0_2 <- diss_qn
}
# Placing the REF names in ascending order according to the distance to the RELEVES i
Rel_order[i,] <- factor(row.names(REF)[order(diss_qn)])
# Placing the distance values in ascending order according to the distance to RELEVES i
Dist_order[i,] <- diss_qn[order(diss_qn)]
# Average distance value
Diss_Mean[i] <- mean(diss_qnw0_2)
# Minimum distance value
Diss_Min[i] <- min(diss_qnw0_2)
}
Output <- list(Diss_Mean,Diss_Min,Rel_order,Dist_order)
names(Output) <- c("Diss_Mean","Diss_Min","RelRef_order","DistRef_order")
return(Output)
}
#' Plot of references and restoration site dissimilarities
#' @description Display similarity link (previously calculated with \code{\link{ChosseRef::DissRef3}} function) between plots and a group of reference plot
#'
#' @param RELEVES Variables of the restoration sites data matrix
#' @param REF Variables of reference sites data matrix
#' @param DISTANCES Object resulting from the analysis of the \code{\link{ChosseRef::DissRef3}} function.
#' @param METHOD Dissimilarity index, partial match to "manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao" or "mahalanobis". Cf \code{\link{vegan::vegdist}} function
#' @param COUL_RELEVES Color of RELEVES names
#' @param COUL_Rel_variable If TRUE: color change for each RELEVES
#' @param COUL_REF Color of REF names
#' @param COUL_Seg Color of links
#' @param COUL_Seg_variable If TRUE: colors change for each link
#' @param LINK_NUMBER Number of link drawn. If "N_REF" then for each RELEVES, the number of link drawn is the number given in \code{N_REF}. If "DIST_MIN"
#' only reference that have distance to restoration sites lower than the value given in \code{DIST_MIN} are drawn.
#' @param N_REF Number of REF to display a link between REF and RELEVES
#' @param DIST_MIN Minimum distance value to draw a link between REF and RELEVES
#' @param VAL_DIST Providing or not (TRUE by default) the distance value display
#' @param DECAL Distance between the printing of the distance and the link
#'
#' @export
#'
#' @examples # ------------ Creating the data needed for the example --------------------
#' library(vegan)
#' data("dune") #downloading of dune data (cf vegan)
#' data("dune.env") #downloading of dune.env data (cf vegan)
#' # keeping only the numeric variables :
#' dune.env <- data.frame(A1 = dune.env$A1,
#' Moisture = as.numeric(as.vector(dune.env$Moisture)),
#' Manure = as.numeric(as.vector(dune.env$Manure)))
#'
#' # Creating a vector indicating which plots are the potential references and which ones are the restored sites
#' sites <- factor(c(rep("Rest",5),rep("Ref",15)))
#' # Creating a vector with the plot names
#' sites_names <- paste(sites,c(1:5,1:15))
#'
#' #Poviding names to rows (useful for the outputs)
#' row.names(dune.env) <- sites_names
#' row.names(dune) <- sites_names
#' dune.envRest <- dune.env[sites=="Rest",]
#' dune.envRef <- dune.env[sites=="Ref",]
#'
#' # -------------- Calculating reference dissimilarities ---------------------
#' Distances <- DissRef3(RELEVES = dune.envRest, REF = dune.envRef, METHOD = "euclidean", DUPLICATES = FALSE)
#' Distances
#'
#' # ---------------- Plotting reference dissimilarities ----------------------
#' Diss_Ref_Plot(RELEVES = dune.envRest, REF = dune.envRef, DISTANCES = Distances, LINK_NUMBER = "N_REF", N_REF = 3)
Diss_Ref_Plot <- function(RELEVES, REF, DISTANCES,
METHOD = "euclidean",
COUL_RELEVES = 2, COUL_Rel_variable = TRUE,
COUL_REF = 1,
COUL_Seg = "#9C8809", COUL_Seg_variable = TRUE,
LINK_NUMBER="absent", N_REF="absent", DIST_MIN="absent",
VAL_DIST = TRUE, DECAL = 0 )
{
# Check that the names of RELEVES and REF are the same:
if(all.equal(names(RELEVES),
names(REF))!=TRUE){warning("REF and RELEVES don't have the same variables")}
# Verification that the information of CHOICE NUMBER, N_REF and DIST_MIN coincide well:
if(LINK_NUMBER!="N_REF" & LINK_NUMBER!="DIST_MIN"){warning("LINK_NUMBER is not correctly assigned")}
if(LINK_NUMBER=="N_REF" & N_REF=="absent"){warning("N_REF is not correctly assigned")}
if(LINK_NUMBER=="DIST_MIN" & DIST_MIN=="absent"){warning("DIST_MIN is not correctly assigned")}
# Verification that DIST_MIN is greater than the minimum distance for each RELEVES:
if(max(DISTANCES$DistRef_order[,1])>DIST_MIN){warning("DIST_MIN is too low and there is no reference close enough for each RELEVES")}
# Fusion of tables
Tableaux <- rbind(RELEVES,REF)
# NMDS analysis:
NMDS <- metaMDS(Tableaux, distance = METHOD, trace = FALSE)
# Display samples of RELEVES and REF
plot(NMDS$points, type="n", main="Choices of reference
with environmental conditions")
text(NMDS$points, labels = row.names(Tableaux),
col = c(rep(COUL_RELEVES, nrow(RELEVES)), rep(COUL_REF, nrow(REF))))
# Creation of the color palette if COUL_Seg_variable = TRUE:
if(COUL_Seg_variable==TRUE){
COUL_Seg <- colorRampPalette(colors=c("#0789e0","#9C093F","#9C0972","#91099C","#57099C","#26099C","#09199C","#09459C","#09819C","#099C8C","#099C5C","#099C26","#199C09","#4A9C09","#7B9C09","#9C9309","#9C6209","#9C3109","#9C0909"))(nrow(DISTANCES$RelRef_order))}else{COUL_Seg <- rep(COUL_Seg,nrow(DISTANCES$RelRef_order))
}
# Loop for each RELEVES:
for (j in 1:nrow(RELEVES))
{
# Coordinates of relevé:
Coo_REL <- NMDS$points[j,c(1:2)]
# If we choose to present a number of references:
if(LINK_NUMBER=="N_REF"){
for(i in 1:N_REF)
{
Num_ref_proche <- c(1:nrow(REF))[row.names(REF)==DISTANCES$RelRef_order[j,i]]
Coo_REF <- NMDS$points[nrow(RELEVES)+Num_ref_proche,c(1:2)]
segments(x0 = Coo_REL[1], y0 = Coo_REL[2], x1 = Coo_REF[1],
y1 = Coo_REF[2], col = COUL_Seg[j])
Dist_REF <- round(DISTANCES$DistRef_order[j,i],2)
if(VAL_DIST==TRUE){text(x = mean(c(Coo_REF[1],Coo_REL[1]))+DECAL,
y = mean(c(Coo_REF[2],Coo_REL[2]))+DECAL,
Dist_REF, col="Grey40", cex = 0.7)}
}
}
# If you choose a distance value for which to display the links:
if(LINK_NUMBER=="DIST_MIN"){
# Number of REF with distance less than DIST_MIN:
N_REF <- length(which(DISTANCES$DistRef_order[1,]<DIST_MIN))
for(i in 1:N_REF)
{
Num_ref_proche <- c(1:nrow(REF))[row.names(REF)==DISTANCES$RelRef_order[j,i]]
Coo_REF <- NMDS$points[nrow(RELEVES)+Num_ref_proche,c(1:2)]
segments(x0 = Coo_REL[1], y0 = Coo_REL[2], x1 = Coo_REF[1],
y1 = Coo_REF[2], col = COUL_Seg[j])
Dist_REF <- round(DISTANCES$DistRef_order[j,i],2)
if(VAL_DIST==TRUE){text(x = mean(c(Coo_REF[1],Coo_REL[1]))+DECAL,
y = mean(c(Coo_REF[2],Coo_REL[2]))+DECAL,
Dist_REF, col="Grey40", cex = 0.7)}
}
}
}
}
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