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Generic function to find the quantiles of a distribution, given the equivalent probability distribution function.

Usage

qgeneric(pdist, p, special = NULL, ...)

Arguments

pdist

Probability distribution function, for example, pnorm for the normal distribution, which must be defined in the current workspace. This should accept and return vectorised parameters and values. It should also return the correct values for the entire real line, for example a positive distribution should have pdist(x)==0 for \(x<0\).

p

Vector of probabilities to find the quantiles for.

special

Vector of character strings naming arguments of the distribution function that should not be vectorised over. Used, for example, for the rate and t arguments in qpexp.

...

The remaining arguments define parameters of the distribution pdist. These MUST be named explicitly.

This may also contain the standard arguments log.p (logical; default FALSE, if TRUE, probabilities p are given as log(p)), and lower.tail (logical; if TRUE (default), probabilities are P[X <= x] otherwise, P[X > x].).

If the distribution is bounded above or below, then this should contain arguments lbound and ubound respectively, and these will be returned if p is 0 or 1 respectively. Defaults to -Inf and Inf respectively.

Value

Vector of quantiles of the distribution at p.

Details

This function is intended to enable users to define "q" functions for new distributions, in cases where the distribution function pdist is available analytically, but the quantile function is not.

It works by finding the root of the equation \(h(q) = pdist(q) - p = 0\). Starting from the interval \((-1, 1)\), the interval width is expanded by 50% until \(h()\) is of opposite sign at either end. The root is then found using uniroot.

This assumes a suitably smooth, continuous distribution.

An identical function is provided in the flexsurv package.

Author

Christopher Jackson <chris.jackson@mrc-bsu.cam.ac.uk>

Examples


qnorm(c(0.025, 0.975), 0, 1)
#> [1] -1.959964  1.959964
qgeneric(pnorm, c(0.025, 0.975), mean=0, sd=1) # must name the arguments
#> [1] -1.959964  1.959964