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PearsonDS (version 1.3.1)

PearsonIV: The Pearson Type IV Distribution

Description

Density, distribution function, quantile function and random generation for the Pearson type IV distribution.

Usage

dpearsonIV(x, m, nu, location, scale, params, log = FALSE)

ppearsonIV(q, m, nu, location, scale, params, lower.tail = TRUE, log.p = FALSE, tol = 1e-08, ...)

qpearsonIV(p, m, nu, location, scale, params, lower.tail = TRUE, log.p = FALSE, tol = 1e-08, ...)

rpearsonIV(n, m, nu, location, scale, params)

Value

dpearsonIV gives the density, ppearsonIV gives the distribution function, qpearsonIV gives the quantile function, and rpearsonIV

generates random deviates.

Arguments

x, q

vector of quantiles.

p

vector of probabilities.

n

number of observations.

m

first shape parameter of Pearson type IV distribution.

nu

second shape parameter (skewness) of Pearson type IV distribution.

location

location parameter of Pearson type IV distribution.

scale

scale parameter of Pearson type IV distribution.

params

vector/list of length 4 containing parameters m, nu, location, scale for Pearson type IV distribution (in this order!).

log, log.p

logical; if TRUE, probabilities p are given as log(p).

lower.tail

logical; if TRUE, probabilities are \(P[X\le x]\), otherwise, \(P[X>x]\).

tol

relative tolerance for evaluation of hypergeometric function 2F1 (ppearsonIV) or absolute target q-error for Newton method (qpearsonIV).

...

further parameters for underlying hypergeometric function.

Warning

If at all possible, package gsl should be installed. Otherwise, calculations for distributions with (more or less) extreme parameters may slow down by factors of more than 1000 and/or may become unstable.

Author

Martin Becker martin.becker@mx.uni-saarland.de, Stefan Klößner S.Kloessner@mx.uni-saarland.de and Joel Heinrich

Details

The Pearson type IV distribution with location parameter location\(=\lambda\), scale parameter scale\(=a\), and shape parameters \(m\) and \(\nu\) can be obtained by its probability density function $$f(x) = \frac{\left|\frac{\Gamma(m+\frac{\nu}{2}i)}{\Gamma(m)}\right|^2} {a B(m-\frac{1}{2},\frac{1}{2})} \left[1+\left(\frac{x-\lambda}{a}\right)^2\right]^{-m} e^{-\nu \mathrm{arctan}\left(\frac{x-\lambda}{a} \right)}$$ for \(a>0\), \(m>\frac{1}{2}\), \(\nu\ne 0\) (\(\nu=0\) corresponds to the Pearson type VII distribution family).

The normalizing constant, which involves the complex Gamma function, is calculated with lngamma_complex (see Gamma) of package gsl, if package gsl is installed. Section 5.1 of [2] contains an algorithm (C code) for the calculation of the normalizing constant, which is used otherwise, but this will be very slow for large absolute values of \(\nu\).

The generation of random numbers (rpearsonIV) uses the C code from section 7 of [2]. It is (thus) restricted to distributions with \(m>1\).

For the cumulative distribution function (ppearsonIV), numerical integration of the density function is used, if package gsl is not available. If package gsl is installed, three different methods are used, depending on the parameter constellation (the corresponding parameter regions were obtained by comprehensive benchmarks):

  • numerical integration of the density function

  • cdf representation of Heinrich [2]

  • cdf representation of Willink [4]

The hypergeometric functions involved in the latter two representations are approximated via partial sums of the corresponding series (see [1], 15.1.1, p. 556). Depending on the parameter constellation, transformation 15.3.5 of [1] (p. 559) is applied for Heinrich's method. The evaluation of the partial sums is first carried out in (ordinary) double arithmetic. If cancellation reduces accuracy beyond tol, the evaluation is redone in double-double arithmetics. If cancellation still reduces accuracy beyond tol, the evaluation is again redone in quad-double arithmetic. Code for double-double and quad-double arithmetics is based on [3]. For Willink's representation, the hypergeometric function in the denominator of \(R\) in equation (10) is evaluated via complex gamma functions (see [1], 15.1.20, p. 556), which is fast and much more stable. A warning is issued if the approximation of the hypergeometric function seems to fail (which should not happen, since numerical integration should be carried out for critical parameter constellations).

The quantile function (qpearsonIV) is obtained via Newton's method. The Newton iteration begins in the (single) mode of the distribution, which is easily calculated (see [2], section 3). Since the mode of the distribution is the only inflection point of the cumulative distribution function, convergence is guaranteed. The Newton iteration stops when the target q-error is reached (or after a maximum of 30 iterations).

References

[1] Abramowitz, M. and I. A. Stegun (1972) Handbook of mathematical functions, National Bureau of Standards, Applied Mathematics Series - 55, Tenth Printing, Washington D.C.

[2] Heinrich, J. (2004) A Guide to the Pearson Type IV Distribution, Univ. Pennsylvania, Philadelphia, Tech. Rep. CDF/Memo/Statistics/Public/6820 http://www-cdf.fnal.gov/physics/statistics/notes/cdf6820_pearson4.pdf

[3] Hida, Y., X. S. Li and D. H. Bailey (2000) Algorithms for quad-double precision floating point arithmetic, Lawrence Berkeley National Laboratory. Paper LBNL-48597.

[4] Willink, R. (2008) A Closed-form Expression for the Pearson Type IV Distribution Function, Aust. N. Z. J. Stat. 50 (2), pp. 199-205

See Also

PearsonDS-package, Pearson

Examples

Run this code
## define Pearson type IV parameter set with m=5.1, nu=3, location=0.5, scale=2
pIVpars <- list(m=5.1, nu=3, location=0.5, scale=2)
## calculate probability density function
dpearsonIV(-2:2,params=pIVpars)
## calculate cumulative distribution function
ppearsonIV(-2:2,params=pIVpars)
## calculate quantile function
qpearsonIV(seq(0.1,0.9,by=0.2),params=pIVpars)
## generate random numbers
rpearsonIV(5,params=pIVpars)

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