platform i386-pc-mingw32
arch i386
os mingw32
system i386, mingw32
status
major 1
minor 5.0
year 2002
month 04
day 29
language R
The function quantile, in base, sometimes gives incorrect results
and is unnecessarily complicated. For example,
> x <- c(-Inf, -Inf, Inf, Inf)
> quantile(x)
0% 25% 50% 75% 100%
-Inf -Inf -Inf Inf Inf
The correct result is
0% 25% 50% 75% 100%
-Inf -Inf NaN Inf Inf
Here the calculation at probs = 0.5 is
50% quantile = x[2] + 0.5 * (x[3] - x[2])
= -Inf + 0.5 * (Inf - (-Inf))
= -Inf + Inf
= NaN
The responsible code is:
n <- length(x)
np <- length(probs)
if(n > 0 && np > 0) {
index <- 1 + (n - 1) * probs
lo <- floor(index)
hi <- ceiling(index)
x <- sort(x, partial = unique(c(lo, hi)))
i <- index > lo
qs <- x[lo]
i <- seq(along=i)[i & !is.na(i)][qs[i] > -Inf]
.minus <- function(x,y) ifelse(x == y, 0, x - y)# ok for Inf - Inf
qs[i] <- qs[i] + .minus(x[hi[i]], x[lo[i]]) * (index[i] - lo[i])
}
I suggest:
n <- length(x)
np <- length(probs)
If(n > 0 && np > 0){
index <- 1 + (n - 1) * probs
lo <- pmin(floor(index), n - 1)
hi <- lo + 1
x <- sort(x, partial = unique(c(lo, hi)))
qs <- x[lo]
dif <- ifelse(x[hi] != qs, x[hi] - qs, 0) # Avoid Inf-Inf NaN.
qs <- ifelse((fac <- location - k) != 0, qs + fac * dif, qs)
}
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