Nothing
bbmm.contour <-
function(x, levels, locations=NULL, plot=TRUE){
#
# x = object of class bbmm
# levels = vector of desired contour levels. E.g., c(85, 90, 95, 99)
# locations = (optional, used if plot=TRUE) x and y coordinates of locations used to estimate Brownian bridge movement model
# plot = logical
#
v <- na.omit(x$probability)
probability <- NULL
for (i in levels) {
contour.z <- function(z) {
abs(i/100 - sum(v[v >= z])/sum(v))
}
probability <- c(probability, optimize(contour.z,
c(0, max(v)), tol = .Machine$double.eps)$minimum)
}
ans <- vector("list", 2)
names(ans) <- c("Contour", "Z")
ans[[1]] <- paste(levels, "%", sep='')
ans[[2]] <- probability
if(plot){
unique.xy <- expand.grid(x=unique(x[[2]]), y=unique(x[[3]]))
temp <- data.frame(x=x[[2]], y=x[[3]], z=x[[4]])
temp <- merge(unique.xy, temp, all.x=TRUE)
temp <- temp[order(temp$x, temp$y),]
temp$z[is.na(temp$z) == TRUE] <- 0
z <- matrix(temp$z, nrow=length(unique(temp$x)),
ncol=length(unique(temp$y)), byrow=TRUE)
contour(x=unique(temp$x), y=unique(temp$y), z, levels=ans[[2]],
drawlabels=TRUE, labels=ans[[1]], xlab="X", ylab="Y")
points(locations[,1], locations[,2], pch=19, cex=0.7, col="red")
lines(locations[,1], locations[,2], col="red")
}
return(ans)
}
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