Nothing
cluster.id <-
function (rl)
{
if(!inherits(rl, "landscape") & !inherits(rl, "metapopulation"))
#if (class(rl)!="landscape" & class(rl)!="metapopulation")
{
stop(paste(rl, " should be either, an object of class class 'landscape' or 'metapopulation'.", sep=""), call. = FALSE)
}
mapsize2 <- rl$mapsize
dist_m2 <- rl$minimum.distance
areaM2 <- rl$mean.area
areaSD2 <- rl$SD.area
Npatch2 <- rl$number.patches
disp2 <- rl$dispersal
rl2 <- rl$nodes.characteristics
ID2 <- rl2$ID
if(inherits(rl, "metapopulation")) occ <- rl2$species
#if (class(rl)=="metapopulation") occ <- rl2$species
rl3 <- rl2[,c(1,2,3,4,8)]
if(nrow(rl3) >= 2)
{
grouping <- hclust(dist(rl3[,1:2], method="euclidean"), "single")
clusters <- cutree(grouping, h=disp2)
} else clusters <- 1
if(inherits(rl, "landscape")) {
#if(class(rl)=="landscape"){
new_2 <- cbind(rl3, clusters)
col1 <- rainbow(max(new_2[,6]))
col2 <- as.data.frame(col1)
col2[,2] <- seq(1:nrow(col2))
col3 <- merge_order(new_2, col2, by.x="clusters",
by.y="V2", sort=FALSE, keep_order=TRUE)
col5 <- nndist (rl3[,1:2])
col4 <- data.frame(col3$x, col3$y, col3$areas, col3$radius,
col3$clusters, col3$col1, col5,
as.numeric(col3$ID))
names(col4)[names(col4)=="col3.x"] <- "x"
names(col4)[names(col4)=="col3.y"] <- "y"
names(col4)[names(col4)=="col3.areas"] <- "areas"
names(col4)[names(col4)=="col3.radius"] <- "radius"
names(col4)[names(col4)=="col3.clusters"] <- "cluster"
names(col4)[names(col4)=="col3.col1"] <- "colour"
names(col4)[names(col4)=="col5"] <- "nneighbour"
names(col4)[names(col4)=="as.numeric.col3.ID."] <- "ID"
rownames(col4) <- 1:nrow(col4)
rland.out <- list(mapsize=mapsize2, minimum.distance=dist_m2,
mean.area=mean(col4$areas), SD.area=sd(col4$areas),
number.patches=nrow(col4), dispersal=disp2,
nodes.characteristics=col4)
class(rland.out) <- "landscape"
return(rland.out)
}
if(inherits(rl, "metapopulation")){
#if(class(rl)=="metapopulation"){
new_2 <- cbind(rl3, clusters)
col1 <- rainbow(max(new_2[,6]))
col2 <- as.data.frame(col1)
col2[,2] <- seq(1:nrow(col2))
col3 <- merge_order(new_2, col2, by.x="clusters",
by.y="V2", sort=FALSE, keep_order=TRUE)
col5 <- nndist (rl3[,1:2])
col4 <- data.frame(col3$x, col3$y, col3$areas, col3$radius,
col3$clusters, col3$col1, col5,
as.numeric(col3$ID), occ)
names(col4)[names(col4)=="col3.x"] <- "x"
names(col4)[names(col4)=="col3.y"] <- "y"
names(col4)[names(col4)=="col3.areas"] <- "areas"
names(col4)[names(col4)=="col3.radius"] <- "radius"
names(col4)[names(col4)=="col3.clusters"] <- "cluster"
names(col4)[names(col4)=="col3.col1"] <- "colour"
names(col4)[names(col4)=="col5"] <- "nneighbour"
names(col4)[names(col4)=="as.numeric.col3.ID."] <- "ID"
names(col4)[names(col4)=="occ"] <- "species"
rownames(col4) <- 1:nrow(col4)
rland.out <- list(mapsize=mapsize2, minimum.distance=dist_m2,
mean.area=mean(col4$areas), SD.area=sd(col4$areas),
number.patches=nrow(col4), dispersal=disp2,
nodes.characteristics=col4)
class(rland.out) <- "metapopulation"
return(rland.out)
}
}
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