Type:	Package
Title:	Pearson Distribution System
Version:	1.3.2
Date:	2025-03-24
Maintainer:	Martin Becker <martin.becker@mx.uni-saarland.de>
Description:	Implementation of the Pearson distribution system, including full support for the (d,p,q,r)-family of functions for probability distributions and fitting via method of moments and maximum likelihood method.
Suggests:	gsl
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
LazyLoad:	yes
Encoding:	UTF-8
NeedsCompilation:	yes
Packaged:	2025-03-24 15:09:05 UTC; mabe003
Author:	Martin Becker [aut, cre], Stefan Klößner [aut], Joel Heinrich [ctb]
Repository:	CRAN
Date/Publication:	2025-03-24 16:20:05 UTC

Pearson Distribution System

Description

Implementation of the d,p,q,r function family, calculation of moments, and fitting via (empirical) moment matching as well as maximum likelihood method for the Pearson distribution system.

Warning

If at all possible, package gsl should be installed. In this case, the functions for Pearson type IV distributions make use of lngamma_complex (see Gamma). If package gsl is not installed, some calculations for Pearson type IV distributions with (more or less) extreme parameters (ie, big nu and/or m) may slow down by factors of more than 1000.

Author(s)

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

Maintainer: Martin Becker martin.becker@mx.uni-saarland.de

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] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 1, Wiley Series in Probability and Mathematical Statistics, Wiley

[5] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 2, Wiley Series in Probability and Mathematical Statistics, Wiley

[6] 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

Pearson for d,p,q,r function family for Pearson distributions, pearsonFitM and pearsonFitML for fitting Pearson distributions, pearsonMSC for model selection, pearsonMoments for calculation of (first four) moments.

Examples

## see documentation of individual functions

The Pearson Distribution System

Description

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

Usage

dpearson(x, params, moments, log = FALSE, ...)

ppearson(q, params, moments, lower.tail = TRUE, log.p = FALSE, ...)

qpearson(p, params, moments, lower.tail = TRUE, log.p = FALSE, ...)

rpearson(n, params, moments, ...)

Arguments

Details

These are the wrapper functions for the (d,p,q,r)-functions of the Pearson distribution system sub-classes.

Value

dpearson gives the density, ppearson gives the distribution function, qpearson gives the quantile function, and rpearson generates random deviates.

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

See Also

PearsonDS-package, Pearson0, PearsonI, PearsonII, PearsonIII, PearsonIV, PearsonV, PearsonVI, PearsonVII, pearsonFitM, pearsonFitML, pearsonMSC

Examples

## Define moments of distribution
moments <- c(mean=1,variance=2,skewness=1,kurtosis=5)
## Generate some random variates
rpearson(5,moments=moments)
## evaluate distribution function
ppearson(seq(-2,3,by=1),moments=moments)
## evaluate density function
dpearson(seq(-2,3,by=1),moments=moments)
## evaluate quantile function
qpearson(seq(0.1,0.9,by=0.2),moments=moments)

The Pearson Type 0 (aka Normal) Distribution

Description

Density, distribution function, quantile function and random generation for the Pearson type 0 (aka Normal) distribution.

Usage

dpearson0(x, mean, sd, params, log = FALSE)

ppearson0(q, mean, sd, params, lower.tail = TRUE, log.p = FALSE)

qpearson0(p, mean, sd, params, lower.tail = TRUE, log.p = FALSE)

rpearson0(n, mean, sd, params)

Arguments

Details

Distributions of type 0 have been added to the Pearson Distribution system in order to have the normal distributions not only nested as limits of other distribution types. The functions are only wrappers for dnorm, pnorm, qnorm and rnorm contained in package stats.

Value

dpearson0 gives the density, ppearson0 gives the distribution function, qpearson0 gives the quantile function, and rpearson0 generates random deviates.

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

See the references in Normal.

See Also

Normal, PearsonDS-package, Pearson

Examples

## define Pearson type 0 parameter set with mean=-1, sd=2
p0pars <- list(mean=-1, sd=2)
## calculate probability density function
dpearson0(-4:1,params=p0pars)
## calculate cumulative distribution function
ppearson0(-4:1,params=p0pars)
## calculate quantile function
qpearson0(seq(0.1,0.9,by=0.2),params=p0pars)
## generate random numbers
rpearson0(5,params=p0pars)

The Pearson Type I (aka Beta) Distribution

Description

Density, distribution function, quantile function and random generation for the Pearson type I (aka Beta) distribution.

Usage

dpearsonI(x, a, b, location, scale, params, log = FALSE)

ppearsonI(q, a, b, location, scale, params, lower.tail = TRUE, 
          log.p = FALSE)

qpearsonI(p, a, b, location, scale, params, lower.tail = TRUE, 
          log.p = FALSE)

rpearsonI(n, a, b, location, scale, params)

Arguments

Details

Essentially, Pearson type I distributions are (location-scale transformations of) Beta distributions, the above functions are thus simple wrappers for dbeta, pbeta, qbeta and rbeta contained in package stats. The probability density function with parameters a, b, scale=s and location=\lambda is given by

f(x)=\frac{1}{|s|}\frac{\Gamma(a+b)}{\Gamma(a)\Gamma(b)}\left(\frac{x-\lambda}{s} \right)^{a-1}\left(1-\frac{x-\lambda}{s}\right)^{b-1}

for a>0, b>0, s\ne 0, 0<\frac{x-\lambda}{s}<1.

Value

dpearsonI gives the density, ppearsonI gives the distribution function, qpearsonI gives the quantile function, and rpearsonI generates random deviates.

Note

Negative values for scale are not excluded, albeit negative skewness is usually obtained by switching a and b (such that a>b) and not by using negative values for scale (and a<b).

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

See the references in Beta.

See Also

Beta, PearsonDS-package, Pearson

Examples

## define Pearson type I parameter set with a=2, b=3, location=1, scale=2
pIpars <- list(a=2, b=3, location=1, scale=2)
## calculate probability density function
dpearsonI(seq(1,3,by=0.5),params=pIpars)
## calculate cumulative distribution function
ppearsonI(seq(1,3,by=0.5),params=pIpars)
## calculate quantile function
qpearsonI(seq(0.1,0.9,by=0.2),params=pIpars)
## generate random numbers
rpearsonI(5,params=pIpars)

The Pearson Type II (aka Symmetric Beta) Distribution

Description

Density, distribution function, quantile function and random generation for the Pearson type II (aka symmetric Beta) distribution.

Usage

dpearsonII(x, a, location, scale, params, log = FALSE)

ppearsonII(q, a, location, scale, params, lower.tail = TRUE, 
           log.p = FALSE)

qpearsonII(p, a, location, scale, params, lower.tail = TRUE, 
           log.p = FALSE)

rpearsonII(n, a, location, scale, params)

Arguments

Details

Essentially, Pearson type II distributions are (location-scale transformations of) symmetric Beta distributions, the above functions are thus simple wrappers for dbeta, pbeta, qbeta and rbeta contained in package stats. The probability density function with parameters a, scale=s and location=\lambda is given by

f(x)=\frac{1}{|s|}\frac{\Gamma(2a)}{\Gamma(a)^2}\left(\frac{x-\lambda}{s}\cdot \left(1-\frac{x-\lambda}{s}\right)\right)^{a-1}

for a>0, s\ne 0, 0<\frac{x-\lambda}{s}<1.

Value

dpearsonII gives the density, ppearsonII gives the distribution function, qpearsonII gives the quantile function, and rpearsonII generates random deviates.

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

See the references in Beta.

See Also

Beta, PearsonDS-package, Pearson

Examples

## define Pearson type II parameter set with a=2, location=1, scale=2
pIIpars <- list(a=2, location=1, scale=2)
## calculate probability density function
dpearsonII(seq(1,3,by=0.5),params=pIIpars)
## calculate cumulative distribution function
ppearsonII(seq(1,3,by=0.5),params=pIIpars)
## calculate quantile function
qpearsonII(seq(0.1,0.9,by=0.2),params=pIIpars)
## generate random numbers
rpearsonII(5,params=pIIpars)

The Pearson Type III (aka Gamma) Distribution

Description

Density, distribution function, quantile function and random generation for the Pearson type III (aka Gamma) distribution.

Usage

dpearsonIII(x, shape, location, scale, params, log = FALSE)

ppearsonIII(q, shape, location, scale, params, lower.tail = TRUE, 
            log.p = FALSE)

qpearsonIII(p, shape, location, scale, params, lower.tail = TRUE, 
            log.p = FALSE)

rpearsonIII(n, shape, location, scale, params)

Arguments

Details

Essentially, the above functions are wrappers for dgamma, pgamma, qgamma and rgamma contained in package stats. As a minor (but important) extension, negative scale parameters (which reflect the distribution at location) are permitted to allow for negative skewness. The probability density function with parameters shape=a, scale=s and location=\lambda is thus given by

f(x)= \frac{1}{|s|^a \Gamma(a)} |x-\lambda|^{a-1} e^{-\frac{x-\lambda}{s}}

for s\ne 0, a>0 and \frac{x-\lambda}{s}\ge 0.

Value

dpearsonIII gives the density, ppearsonIII gives the distribution function, qpearsonIII gives the quantile function, and rpearsonIII generates random deviates.

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

See the references in GammaDist.

See Also

GammaDist, PearsonDS-package, Pearson

Examples

## define Pearson type III parameter set with shape=3, location=1, scale=-2
pIIIpars <- list(shape=3, location=1, scale=-0.5)
## calculate probability density function
dpearsonIII(-4:1,params=pIIIpars)
## calculate cumulative distribution function
ppearsonIII(-4:1,params=pIIIpars)
## calculate quantile function
qpearsonIII(seq(0.1,0.9,by=0.2),params=pIIIpars)
## generate random numbers
rpearsonIII(5,params=pIIIpars)

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)

Arguments

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).

Value

dpearsonIV gives the density, ppearsonIV gives the distribution function, qpearsonIV gives the quantile function, and rpearsonIV generates random deviates.

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.

Note

Many calculations are done in logarithms to avoid IEEE 754 underflow/overflow issues.

The description of quad-double arithmetics in [3] contains minor errors: in algorithm 9 (p. 6), lines 9 and 10 should be interchanged; in algorithm 14 (p. 10), k<2 should be replaced with k<3 (line 10) and k<3 should be replaced with k<4 (line 11).

Author(s)

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

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

## 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)

The Pearson Type V (aka Inverse Gamma) Distribution

Description

Density, distribution function, quantile function and random generation for the Pearson type V (aka Inverse Gamma) distribution.

Usage

dpearsonV(x, shape, location, scale, params, log = FALSE)

ppearsonV(q, shape, location, scale, params, lower.tail = TRUE, 
          log.p = FALSE)

qpearsonV(p, shape, location, scale, params, lower.tail = TRUE, 
          log.p = FALSE)

rpearsonV(n, shape, location, scale, params)

Arguments

Details

The Pearson type V distributions are essentially Inverse Gamma distributions. Thus, all functions are implemented via calls to the corresponding functions for Gamma distributions, ie. dgamma, pgamma, qgamma and rgamma in package stats. Negative scale parameters (which reflect the distribution at location) are permitted to allow for negative skewness. The probability density function with parameters shape=a, scale=s and location=\lambda is given by

f(x)= \frac{|s|^a }{\Gamma(a)} |x-\lambda|^{-a-1} e^{-\frac{s}{x-\lambda}}

for s\ne 0, a>0 and \frac{s}{x-\lambda}> 0.

Value

dpearsonV gives the density, ppearsonV gives the distribution function, qpearsonV gives the quantile function, and rpearsonV generates random deviates.

Note

Since package version 0.98, the parameter scale corresponds to the usual scale parameter of the Inverse Gamma distribution (not the reciprocal value, which was implemented [incorrectly!] until package version 0.97).

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

See the references in GammaDist.

See Also

GammaDist, PearsonDS-package, Pearson

Examples

## define Pearson type V parameter set with shape=3, location=1, scale=-2
pVpars <- list(shape=3, location=1, scale=-2)
## calculate probability density function
dpearsonV(-4:1,params=pVpars)
## calculate cumulative distribution function
ppearsonV(-4:1,params=pVpars)
## calculate quantile function
qpearsonV(seq(0.1,0.9,by=0.2),params=pVpars)
## generate random numbers
rpearsonV(5,params=pVpars)

The Pearson Type VI (aka Beta Prime) Distribution

Description

Density, distribution function, quantile function and random generation for the Pearson type VI (aka Beta prime) distribution.

Usage

dpearsonVI(x, a, b, location, scale, params, log = FALSE)

ppearsonVI(q, a, b, location, scale, params, lower.tail = TRUE, 
           log.p = FALSE)

qpearsonVI(p, a, b, location, scale, params, lower.tail = TRUE, 
           log.p = FALSE)

rpearsonVI(n, a, b, location, scale, params)

Arguments

Details

Pearson type VI distributions are (location-scale transformations of) Beta prime distributions, and Beta prime distributions are scaled F-distributions. The above functions are thus implemented via calls to df, pf, qf and rf (contained in package stats). The probability density function with parameters a, b, scale=s and location=\lambda is given by

f(x)=\frac{\Gamma(a+b)}{|s|\Gamma(a)\Gamma(b)}\left(\frac{x-\lambda}{s} \right)^{a-1}\left(1+\frac{x-\lambda}{s}\right)^{-a-b}

for a>0, b>0, s\ne 0, \frac{x-\lambda}{s}>0.

Value

dpearsonVI gives the density, ppearsonVI gives the distribution function, qpearsonVI gives the quantile function, and rpearsonVI generates random deviates.

Note

Negative values for scale are permitted to allow for negative skewness.

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

See the references in FDist.

See Also

FDist, PearsonDS-package, Pearson

Examples

## define Pearson type VI parameter set with a=2, b=3, location=1, scale=2
pVIpars <- list(a=2, b=3, location=1, scale=2)
## calculate probability density function
dpearsonVI(seq(1,6,by=1),params=pVIpars)
## calculate cumulative distribution function
ppearsonVI(seq(1,6,by=1),params=pVIpars)
## calculate quantile function
qpearsonVI(seq(0.1,0.9,by=0.2),params=pVIpars)
## generate random numbers
rpearsonVI(5,params=pVIpars)

The Pearson Type VII (aka Student's t) Distribution

Description

Density, distribution function, quantile function and random generation for the Pearson type VII (aka Student's t) distribution.

Usage

dpearsonVII(x, df, location, scale, params, log = FALSE)

ppearsonVII(q, df, location, scale, params, lower.tail = TRUE, 
            log.p = FALSE)

qpearsonVII(p, df, location, scale, params, lower.tail = TRUE, 
            log.p = FALSE)

rpearsonVII(n, df, location, scale, params)

Arguments

Details

The Pearson type VII distribution is a simple (location-scale) transformation of the well-known Student's t distribution; the probability density function with parameters df=n, location=\lambda and scale=s is given by

f(x) = \frac{1}{|s|}\frac{\Gamma(\frac{n+1}{2})}{\sqrt{n \pi} \Gamma(\frac{n}{2})} \left(1 + \frac{(\frac{x-\lambda}{s})^2}{n}\right)^{-\frac{n+1}{2}}

for s\ne 0. The above functions are thus only wrappers for dt, pt, qt and rt contained in package stats.

Value

dpearsonVII gives the density, ppearsonVII gives the distribution function, qpearsonVII gives the quantile function, and rpearsonVII generates random deviates.

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

See the references in TDist.

See Also

TDist, PearsonDS-package, Pearson

Examples

## define Pearson type VII parameter set with df=7, location=1, scale=1
pVIIpars <- list(df=7, location=1, scale=1)
## calculate probability density function
dpearsonVII(-2:4,params=pVIIpars)
## calculate cumulative distribution function
ppearsonVII(-2:4,params=pVIIpars)
## calculate quantile function
qpearsonVII(seq(0.1,0.9,by=0.2),params=pVIIpars)
## generate random numbers
rpearsonVII(5,params=pVIIpars)

Empirical Moments

Description

Calculates the first four empirical moments (mean, variance, skewness, kurtosis) of a numeric vector.

Usage

empMoments(x)

Arguments

Value

Named vector of length 4 containing mean, variance, skewness and kurtosis (in this order).

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

See Also

PearsonDS-package

Examples

## Generate sample with given (theoretical) moments
DATA <- rpearson(25000,moments=c(mean=1,variance=2,skewness=1,kurtosis=5))
## Calculate corresponding empirical moments
empMoments(DATA)

Fitting (Incomplete) Set of Moments to Given Distribution Type

Description

For a given incomplete (skewness or kurtosis are missing) set of moments, the complete set of moments (mean, variance, skewness, and kurtosis) is calculated, using a given distribution type (if possible). Either the complete set of moments or the distribution parameters are returned.

Usage

matchMoments(mean, variance, skewness = NA, kurtosis = NA, type, moments,
             skewness.sign = c("+", "-"), return.distribution = FALSE)

Arguments

Value

If return.distribution==TRUE: list of parameters for Pearson distribution. First entry gives type of distribution (0 for type 0, 1 for type I, ..., 7 for type VII), remaining entries give distribution parameters (depending on distribution type).

If return.distribution==FALSE: numeric vector with named elements mean, variance, skewness, kurtosis corresponding to a Pearson distribution of type type.

Note

If the specified subset of moments does not match the distribution type or if too many moments are missing, an error is issued.

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

[1] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 1, Wiley Series in Probability and Mathematical Statistics, Wiley

[2] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 2, Wiley Series in Probability and Mathematical Statistics, Wiley

See Also

PearsonDS-package, Pearson, pearsonFitM, pearsonMoments

Examples

matchMoments(mean=0,variance=1,kurtosis=4.5,type=3,return.distribution = TRUE)
matchMoments(mean=0,variance=1,kurtosis=4.5,type="III")
matchMoments(mean=0,variance=1,kurtosis=4.5,type="III",skewness.sign="-")

matchMoments(mean=0,variance=1,skewness=-2,type="III",return.distribution = TRUE)
pearsonFitM(moments=matchMoments(mean=0,variance=1,skewness=-2,type="III"))
matchMoments(mean=0,variance=1,skewness=-2,type="III")

Regions of Pearson Distribution Types

Description

2D-Plot of the regions for the different types of Pearson distributions, depending on (squared) skewness and kurtosis.

Usage

pearsonDiagram(max.skewness = sqrt(14), max.kurtosis = 24, 
               squared.skewness = TRUE, lwd = 2, legend = TRUE, 
               n = 301)

Arguments

Details

The label of the x-axis is \beta_1 for squared skewness and \sqrt{\beta_1} for regular skewness. The label of the y-axis is \beta_2.

Value

Nothing useful. Function called for its side-effects.

Author(s)

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

References

[1] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 1, Wiley Series in Probability and Mathematical Statistics, Wiley

See Also

PearsonDS-package

Examples

## Show me the regions for the different distribution types!
pearsonDiagram()

Method of Moments Estimator for Pearson Distributions

Description

This function calculates the method of moments estimator for Pearson distribution, ie, it generates a Pearson distribution with moments exactly (up to rounding errors) matching the input moments mean, variance, skewness and kurtosis.

Usage

pearsonFitM(mean, variance, skewness, kurtosis, moments)

Arguments

Value

List of parameters for Pearson distribution. First entry gives type of distribution (0 for type 0, 1 for type I, ..., 7 for type VII), remaining entries give distribution parameters (depending on distribution type).

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

[1] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 1, Wiley Series in Probability and Mathematical Statistics, Wiley

[2] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 2, Wiley Series in Probability and Mathematical Statistics, Wiley

See Also

PearsonDS-package, Pearson, pearsonFitML, pearsonMoments, pearsonMSC

Examples

## Define moments of distribution
moments <- c(mean=1,variance=2,skewness=1,kurtosis=5)
## find Pearson distribution with these parameters
ppar <- pearsonFitM(moments=moments)
print(unlist(ppar))
## check moments
pearsonMoments(params=ppar)

Maximum Likelihood Estimation of Pearson Distributions

Description

This function tries to find the Maximum Likelihood estimator within the Pearson distribution system. ML estimation is done for all sub-classes of the distribution system via numerical optimization (with nlminb). The sub-class with the optimal likelihood function value and the corresponding parameters are returned.

Usage

pearsonFitML(x, ...)

Arguments

Details

Starting values for each sub-class are found in a three-step procedure. First, the empirical moments of the input vector are calculated. In the second step, the moments are altered, such that the moment restrictions for the current sub-class are fulfilled (if necessary), and the method of moments estimator is calculated to obtain starting values for the optimizer. In the last step, the starting values are adjusted (if necessary) in order to assure that the whole sample lies in the support of the distribution.

Value

List of parameters for Pearson distribution. First entry gives type of distribution (0 for type 0, 1 for type I, ..., 7 for type VII), remaining entries give distribution parameters (depending on distribution type).

Note

The implementation is VERY preliminary (and slow). No analytical results are used, ie. no analytical solutions for ML estimators and no analytical gradients. Most of the distribution types (0, II, III, V, VII) should rather be neglected (for speed reasons), because they will contain the MLE with probability of 0.

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

[1] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 1, Wiley Series in Probability and Mathematical Statistics, Wiley

[2] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 2, Wiley Series in Probability and Mathematical Statistics, Wiley

See Also

PearsonDS-package, Pearson, pearsonFitM, pearsonMSC, pearsonMoments

Examples

## Generate sample 
DATA <- rpearson(1000,moments=c(mean=1,variance=2,skewness=1,kurtosis=5))
## find Pearson distribution with these parameters
ppar <- pearsonFitML(DATA)
print(unlist(ppar))
## compare with method of moments estimator
print(unlist(pearsonFitM(moments=empMoments(DATA))))

Log-Likelihoods and Model Selection Criteria for different Pearson distribution types

Description

This function performs (as pearsonFitML) an ML estimation for all sub-classes of the Pearson distribution system via numerical optimization (with nlminb) for model selection purposes. Apart from calculating the log-likelihood values as well as the values of some common model selection criteria (pure ML, AIC, AICc, BIC, HQC) for the different sub-classes, model selection is done for each of the criteria and the parameter estimates for each distribution sub-class are returned.

Usage

pearsonMSC(x, ...)

Arguments

Details

For the ML estimation, see the details of pearsonFitML. The considered Model Selection Criteria (MSCs) are 'pure' Maximum Likelihood (ML), Akaike Information Criterion (AIC), corrected AIC (AICc), Bayes Information Criterion (BIC, also known as Schwarz Criterion), and Hannan-Quinn-Criterion (HQC). The definitions used for the different MSCs are

• for ML: -2\cdot \ln L(\theta)

• for AIC: -2\cdot \ln L(\theta)+2\cdot k

• for AICc: -2\cdot \ln L(\theta)+2\cdot k\cdot\frac{n}{n-k-1}

• for BIC: -2\cdot \ln L(\theta)+ k\cdot \ln(n)

• for HQC: -2\cdot \ln L(\theta)+2\cdot k\cdot \ln(\ln(n))

where \ln L(\theta) denotes the log-Likelihood, n denotes the number of observations (ie, the length of x) and k denotes the number of parameters of the distribution (sub-class).

The best model minimizes the corresponding MSC function values.

Value

A list containing

Note

The implementation is still preliminary (and slow). No analytical results are used, ie. no analytical solutions for ML estimators and no analytical gradients.

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

See Also

PearsonDS-package, Pearson, pearsonFitML

Examples

## Generate sample 
DATA <- rpearson(500,moments=c(mean=1,variance=2,skewness=1,kurtosis=5))
## Call pearsonMSC for model selection
MSC  <- pearsonMSC(DATA,control=list(iter.max=1e5,eval.max=1e5))
## log-Likelihood values for all distribution sub-classes
print(MSC$logLik)
## Values for all MSCs and distribution sub-classes
print(MSC$MSCs)
## Model selection for all MSCs
print(MSC$Best)

Moments of Pearson Distribution

Description

Calculates the first four moments (mean, variance, skewness, kurtosis) of a Pearson distribution.

Usage

pearsonMoments(params, moments)

Arguments

Value

Named vector of length 4 containing mean, variance, skewness and kurtosis (in this order).

Note

Optional parameter moments is merely for testing purposes. Of course, pearsonMoments should reproduce its input (when neglecting rounding errors) if moments is given.

kurtosis is the kurtosis of the distribution, not the excess kurtosis!

Author(s)

Martin Becker martin.becker@mx.uni-saarland.de

References

[1] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 1, Wiley Series in Probability and Mathematical Statistics, Wiley

[2] Johnson, N. L., Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, Vol. 2, Wiley Series in Probability and Mathematical Statistics, Wiley

See Also

PearsonDS-package, Pearson0, PearsonI, PearsonII, PearsonIII, PearsonIV, PearsonV, PearsonVI, PearsonVII,

Examples

## Define moments of distribution
moments <- c(mean=1,variance=2,skewness=1,kurtosis=5)
## Are the moments reproduced?
pearsonMoments(moments=moments)