.BG
.FN quantile
.TL
Empirical Quantiles
.CS
quantile(x, probs=seq(0,1,.25))
.AG x
vector of data.
.AG probs
vector of desired probability levels.
Values must be between 0 and 1 inclusive.
The default produces a \(``five number summary\('':
the minimum, lower quartile, median, upper quartile, and maximum
of `x'.
.RT
vector of empirical quantiles corresponding to the
`probs' levels in the sorted `x' data.
.SP
.PP
The algorithm linearly interpolates between order statistics of `x',
assuming that the `i'th order statistic is the `(i-.5)/length(x)' quantile.
The algorithm uses partial sorting, hence is quickly able to find
a few quantiles even of large datasets.
.EX
quantile(mydata)	# five number summary
quantile(xxx,c(.33,.67))  # 33% and 67% points of xxx
.KW math
.WR
