kuantile: Quicker Sample Quantiles

Description

The function 'kuantile' computes sample quantiles corresponding to the specified probabilities. The intent is to mimic the generic (base) function 'quantile' but using a variant of the Floyd and Rivest (1975) algorithm which is somewhat quicker, especially for large sample sizes.

Usage

kuantile(x, probs = seq(0, 1, .25), na.rm = FALSE, names = TRUE, type = 7, ...)

Arguments

numeric vector whose sample quantiles are wanted.

probs

numeric vector of probabilities with values in [0,1].

type

an integer between 1 and 9 selecting one of the nine quantile algorithms detailed below to be used.

na.rm

logical: if true, any 'NA' and 'NaN''s are removed from 'x' before the quantiles are computed.

names

logical: if true, the result has a 'names' attribute.

...

further arguments passed to or from other methods.

Value

A vector of quantiles of the same length as the vector p.

Details

A vector of length 'length(p)' is returned. See the documentation for 'quantile' for further details on the types. The algorithm was written by K.C. Kiwiel. It is a modified version of the (algol 68) SELECT procedure of Floyd and Rivest (1975), incorporating modifications of Brown(1976). The algorithm has linear growth in the number of comparisons required as sample size grows. For the median, average case behavior requires $1.5 n + O((n log n)^{1/2})$ comparisons. See Kiwiel (2005) and Knuth (1998) for further details. When the number of required elements of p is large, it may be preferable to revert to a full sort.

References

R.W. Floyd and R.L. Rivest: "Algorithm 489: The Algorithm SELECT---for Finding the $i$th Smallest of $n$ Elements", Comm. ACM 18, 3 (1975) 173,

T. Brown: "Remark on Algorithm 489", ACM Trans. Math. Software 3, 2 (1976), 301-304.

K.C. Kiwiel: On Floyd and Rivest's SELECT Algorithm, Theoretical Computer Sci. 347 (2005) 214-238.

D. Knuth, The Art of Computer Programming, Volume 3, Sorting and Searching, 2nd Ed., (1998), Addison-Wesley.

Examples

Run this code

# NOT RUN {
     kuantile(x <- rnorm(1001))# Extremes & Quartiles by default

     ### Compare different types
     p <- c(0.1,0.5,1,2,5,10,50)/100
     res <- matrix(as.numeric(NA), 9, 7)
     for(type in 1:9) res[type, ] <- y <- kuantile(x,  p, type=type)
     dimnames(res) <- list(1:9, names(y))
     ktiles <- res

     ### Compare different types
     p <- c(0.1,0.5,1,2,5,10,50)/100
     res <- matrix(as.numeric(NA), 9, 7)
     for(type in 1:9) res[type, ] <- y <- quantile(x,  p, type=type)
     dimnames(res) <- list(1:9, names(y))
     qtiles <- res

     max(abs(ktiles - qtiles))


# }

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