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# Copyright 2000-2001 (c) Nicholas Lewin-Koh
# modifications 2001-2003 (c) Roger Bivand
# Returns a vector of HSV values
# modifications 2003 (c) Renaud Lancelot
color.ramp <- function (nclass, color = "red", nvec = NULL, type = "q"){
eq.split <- function(ncl){
mult <- rep((1 / ncl), (ncl - 1))
mult * seq(1, (ncl - 1))
}
color.list <- list(cname = c("blue", "green", "yellow", "red"), hsvcol = c(0.7, 0.375, 0.19, 0))
cind <- match(color, color.list$cname)
### change from "if(nvec)" to "if(!is.null(nvec))"
if(!is.null(nvec)){
if(type == "q"){
pr <- eq.split(nclass)
### changes in min, quantile and max
brks <- c(min(nvec, na.rm = TRUE),
quantile(nvec, pr, names = FALSE, na.rm = TRUE),
max(nvec, na.rm = TRUE))
brks <- unique(brks)
classvec <- cut(nvec, brks, labels = FALSE, include.lowest = TRUE)
ramp <- hsv(rep(color.list$hsvcol[cind], nclass), c(pr, 1))
return(list(ramp = ramp, col.class = classvec, breaks=brks))
}
else
if(type == "e"){
pr <- eq.split(nclass)
### changes in min, range and max
brks <- c(min(nvec, na.rm = TRUE),
pr * diff(range(nvec, na.rm = TRUE)),
max(nvec, na.rm = TRUE))
brks <- unique(brks)
classvec <- cut(nvec, brks, labels = FALSE, include.lowest = TRUE)
ramp <- hsv(rep(color.list$hsvcol[cind], nclass), c(pr, 1))
return(list(ramp = ramp, col.class = classvec, breaks=brks))
}
}
return(NULL)
}
leglabs <- function(vec, under="under", over="over", between="-") {
x <- vec
lx <- length(x)
if(lx < 3) stop("vector too short")
res <- character(lx-1)
res[1] <- paste(under, x[2])
for (i in 2:(lx-2)) res[i] <- paste(x[i], between, x[i+1])
res[lx-1] <- paste(over, x[lx-1])
res
}
#The set of classification methods is large (Dent p. 145), but there are a few to remember:
# * Equal Intervals ("Constant Interval"): each class has same difference in value
# * Quantile (N-tile): each class has same number of units
# * Natural Breaks: visual examination; manual determination
# Then a lot of ones that you might need to use once in a while
# Arithmetic progression: constant increase (decrease) in "width" of class
# Geometric progression: constant multiplier used to derive width of class
# Jenk's Iterative ("optimal") minimize within class standard deviations (variance) [ESRI calls this "natural breaks"]
# (see Dent 147-149 on use of F-ratio and weighting)
# Arbitrary breaks: given externally (laws, regulations, natural process)
# Standard deviations: statistical distribution
# Nested Means works by successive halving at the mean (2,4,8,16, ...)
#(Chrisman)
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