tggd (version 0.1.1)

tggd: The Truncated Generalised Gamma Distribution

Description

Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution in linear space.

Usage

dtggd(x, scale=1e14, a=-1, b=1, xmin=1e10, log = FALSE)
ptggd(q, scale=1e14, a=-1, b=1, xmin=1e10, lower.tail = TRUE, log.p = FALSE)
qtggd(p, scale=1e14, a=-1, b=1, xmin=1e10, lower.tail = TRUE, log.p = FALSE,
res.approx=1e-2)
rtggd(n, scale=1e14, a=-1, b=1, xmin=1e10, res.approx=1e-2)

Value

dtggd gives the density, ptggd gives the distribution function, qtggd gives the quantile function, and rtggd generates random deviates.

Invalid arguments will result in return value NaN, with a warning.

The length of the result is determined by n for rtggd, and is the maximum of the lengths of the numerical arguments for the other functions.

The numerical arguments other than n are recycled to the length of the result. Only the first elements of the logical arguments are used.

Arguments

x,q

Vector of quantiles.

p

Vector of probabilities.

n

Number of observations. If length(n) > 1, the length is taken to be the number required.

scale

Vector of values for scale, which controls the transition regime between the power law slope and the exponential cut-off of the TGGD. This is analogous to the scale parameter for the standard Gamma distribution (see GammaDist).

a

Vector of values for a, which controls the power law slope of the TGGD.

b

Vector of values for b, which controls the exponential cutoff of the TGGD.

xmin

Vector of values for xmin, which controls the lower limit at which to trancate the TGGD.

res.approx

The resolution used to create the inverted CDF required to map probability integrals back onto quantiles.

log, log.p

Logical; if TRUE, probabilities/densities p are returned as log(p).

lower.tail

Logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

Author

Aaron Robotham, Steven Murray

Details

This distribution function is described in detail in Murray, Robotham and Power 2016. The PDF is given by:

$$f(x;a,b,s,m) = \frac{b({\frac{x}{s}})^a \exp(-({\frac{x}{s}})^b)}{s \Gamma(\frac{a+1}{b},(\frac{m}{s})^b)}$$

where, from the argument list above, we use x=x, a=a, b=b, s=scale and m=xmin. \(\Gamma\) is the upper incomplete Gamma function as defined by the gsl gamma_inc function, using the same argument ordering, where gamma_inc(a,x)==pgamma(x,a,lower=FALSE)*gamma(x) for a>0. gamma_inc is used because it allows for the computation of upper incomplete integrals in cases where a<=0.

References

Murray, Robotham and Power (2016)

See Also

GammaDist regarding the Gamma distribution. RNG about random number generation in R. Distributions for other standard distributions.

Examples

r <- rtggd(100,a=-2)
hist(log10(r))

##Ideally the output below should equal 1, in practice it will be very close:
qtggd(ptggd(r))/r

#These should be the same:
integrate(dtggd,lower=1e10,upper=1e11,a=-1.5,b=0.7,xmin=1e10)
ptggd(1e11,a=-1.5,b=0.7,xmin=1e10)

#This should be very close to 1 (for a true PDF):
ptggd(1e18,a=-1.5,b=0.7,xmin=1e10)

#To show the link to the log10 (called log) and ln variants (and the slight inaccuracies)
#these outputs should be a sequence from 0 to 1 (by=0.1):
ptggd(10^qtggd_log(seq(0,1,by=0.1)))
ptggd(exp(qtggd_ln(seq(0,1,by=0.1))))