Computes weighted quantiles. whdquantile() uses a weighted
Harrell-Davis quantile estimator. wthdquantile() uses a weighted
trimmed Harrell-Davis quantile estimator. wquantile() uses a weighted
traditional quantile estimator.
Usage
kish_ess(w)
wquantile_generic(x, w, probs, cdf)
whdquantile(x, w, probs)
wthdquantile(x, w, probs, width = 1/sqrt(kish_ess(w)))
wquantile(x, w, probs, type = 7)Arguments
- w
Numeric vector of weights to give each value. Should be the same length as the vector of values.
- x
Numeric vector of values of which to determine the quantiles.
- probs
Numeric vector of the quantiles to retrieve.
- cdf
Cumulative distribution function.
- width
Numeric value for the width of the interval in the trimmed Harrell-Davis quantile estimator.
- type
Numeric value for type of weighted quantile.
Examples
mydata <- c(1:100, 1000)
mydataWithNAs <- mydata
mydataWithNAs[c(1,5,7)] <- NA
weights <- rep(1, length(mydata))
quantiles <- c(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99)
whdquantile(
x = mydata,
w = weights,
probs = quantiles)
#> [1] 1.588365 5.550019 10.600000 25.750000 51.000000 76.250000 91.400042
#> [8] 99.033690 662.561505
wthdquantile(
x = mydata,
w = weights,
probs = quantiles)
#> [1] 1.588098 5.464074 10.330683 25.435245 51.000000 76.564755 91.669317
#> [8] 97.306006 662.576876
wquantile(
x = mydata,
w = weights,
probs = quantiles)
#> [1] 2 6 11 26 51 76 91 96 100
whdquantile(
x = mydataWithNAs,
w = weights,
probs = quantiles)
#> [1] 2.626940 7.830704 13.291418 28.000000 52.500000 77.000000 91.700087
#> [8] 99.966562 674.296651
wthdquantile(
x = mydataWithNAs,
w = weights,
probs = quantiles)
#> [1] 2.620986 7.751954 13.041100 27.691494 52.500000 77.308506 91.958900
#> [8] 97.816240 672.742321
wquantile(
x = mydataWithNAs,
w = weights,
probs = quantiles)
#> [1] 2.97 8.85 13.70 28.25 52.50 76.75 91.30 96.15 127.00
