dither: Function to randomly perturb a vector

In quantreg: Quantile Regression

Function to randomly perturb a vector

Description

With malice aforethought, dither adds a specified random perturbation to each element of the input vector, usually employed as a device to mitigate the effect of ties.

Usage

dither(x, type = "symmetric", value = NULL)

Arguments

x	x a numeric vector
type	type is either 'symmetric' or 'right'
value	value scale of dequantization

Details

The function dither operates slightly differently than the function jitter in base R, permitting strictly positive perturbations with the option type = "right" and using somewhat different default schemes for the scale of the perturbation. Dithering the response variable is frequently a useful option in quantile regression fitting to avoid deleterious effects of degenerate solutions. See, e.g. Machado and Santos Silva (2005). For a general introduction and some etymology see the Wikipedia article on "dither". For integer data it is usually advisable to use value = 1. When 'x' is a matrix or array dither treats all elements as a vector but returns an object of the original class.

Value

A dithered version of the input vector 'x'.

Note

Some further generality might be nice, for example something other than uniform noise would be desirable in some circumstances. Note that when dithering you are entering into the "state of sin" that John von Neumann famously attributed to anyone considering "arithmetical methods of producing random digits." If you need to preserve reproducibility, then set.seed is your friend.

Author(s)

R. Koenker

References

Machado, J.A.F. and Santos Silva, J.M.C. (2005), Quantiles for Counts, Journal of the American Statistical Association, vol. 100, no. 472, pp. 1226-1237.

See Also

jitter

Examples

x <- rlnorm(40)
y <- rpois(40, exp(.5 + log(x)))
f <- rq(dither(y, type = "right", value = 1) ~ x)

quantreg documentation built on Aug. 19, 2023, 5:09 p.m.