StableDistribution {stabledist}

Stable Distribution Function

Description

A collection and description of functions to compute density, distribution and quantile function and to generate random variates of the stable distribution.

The four functions are:

[dpqr]stable

the (skewed) stable distribution.

Usage

dstable(x, alpha, beta, gamma = 1, delta = 0, pm = 0,
        log = FALSE,
        tol = 64*.Machine$double.eps, zeta.tol = NULL,
        subdivisions = 1000)
pstable(q, alpha, beta, gamma = 1, delta = 0, pm = 0,
        lower.tail = TRUE, log.p = FALSE, silent = FALSE,
        tol = 64*.Machine$double.eps, subdivisions = 1000)
qstable(p, alpha, beta, gamma = 1, delta = 0, pm = 0,
        lower.tail = TRUE, log.p = FALSE,
        tol = .Machine$double.eps^0.25, maxiter = 1000, trace = 0,
        integ.tol = 1e-7, subdivisions = 200)
rstable(n, alpha, beta, gamma = 1, delta = 0, pm = 0)

Arguments

alpha, beta, gamma, delta

value of the index parameter alpha in the interval= (0, 2]; skewness parameter beta, in the range [-1, 1]; scale parameter gamma; and location (or ‘shift’) parameter delta.

n

sample size (integer).

p

numeric vector of probabilities.

pm

parameterization, an integer in 0, 1, 2; by default pm=0, the ‘S0’ parameterization.

x, q

numeric vector of quantiles.

log, log.p

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

lower.tail

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

silent

logical indicating that e.g., warnings should be suppressed when NaN is produced (because of numerical problems).

integ.tol

positive number, the tolerance used for numerical integration, see integrate.

tol

numerical tolerance,

dstable(), pstable():

used for numerical integration, see integ.tol above. Note that earlier versions had tighter tolerances – which seem too tight as default values.

qstable():

used for rootfinding, see uniroot.

zeta.tol

(dstable) numerical tolerance for checking if x is close to \zeta(\alpha,\beta). The default, NULL depends itself on (\alpha,\beta).
As it is experimental and not guaranteed to remain in the future, its use is not recommended in production code. Rather e-mail the package maintainer about it.

subdivisions

maximal number of intervals for integration, see integrate.

maxiter, trace

maximal number of iterations and verboseness in uniroot, see there.

Details

Skew Stable Distribution:

The function uses the approach of J.P. Nolan for general stable distributions. Nolan (1997) derived expressions in form of integrals based on the characteristic function for standardized stable random variables. For dstable and pstable, these integrals are numerically evaluated using R's integrate() function.
“S0” parameterization [pm=0]: based on the (M) representation of Zolotarev for an alpha stable distribution with skewness beta. Unlike the Zolotarev (M) parameterization, gamma and delta are straightforward scale and shift parameters. This representation is continuous in all 4 parameters, and gives an intuitive meaning to gamma and delta that is lacking in other parameterizations.
Switching the sign of beta mirrors the distribution at the vertical axis x = \delta, i.e.,

f(x, \alpha, -\beta, \gamma, \delta, 0) = f(2\delta-x, \alpha, +\beta, \gamma, \delta, 0),

see the graphical example below.

“S” or “S1” parameterization [pm=1]: the parameterization used by Samorodnitsky and Taqqu in the book Stable Non-Gaussian Random Processes. It is a slight modification of Zolotarev's (A) parameterization.
“S*” or “S2” parameterization [pm=2]: a modification of the S0 parameterization which is defined so that (i) the scale gamma agrees with the Gaussian scale (standard dev.) when alpha=2 and the Cauchy scale when alpha=1, (ii) the mode is exactly at delta. For this parametrization, stableMode(alpha,beta) is needed.
“S3” parameterization [pm=3]: an internal parameterization, currently not available for these functions. The scale is the same as the “S2” parameterization, the shift is -\beta*g(\alpha), where g(\alpha) is defined in Nolan(1999).

Value

All values for the *stable functions are numeric vectors: d* returns the density, p* returns the distribution function, q* returns the quantile function, and r* generates random deviates.

Tail Behavior

The asymptotic behavior for large x, aka “tail behavior” for the cumulative F(x) = P(X \le x) is (for x\to\infty)

1 - F(x) \sim (1+\beta) C_\alpha x^{-\alpha},

where C_\alpha = \Gamma(\alpha)/\pi \sin(\alpha\pi/2) ; hence also

F(-x) \sim (1-\beta) C_\alpha x^{-\alpha}.

Differentiating F() above gives

f(x) \sim \alpha(1+\beta) C_\alpha x^{-(1+\alpha)}.

Note

In the case \beta = 1, the distributions are “maximally skewed to the right” or simply “extremal stable” (Zolotarev). In that case, the package FMStable provides dpq* functions which are faster and more accurate than ours (if accuracy higher than about 6 digits is needed), see, pEstable.

When alpha is close to 1 or close to 0 (“close”, e.g., meaning distance d < 0.01), the computations typically are numerically considerably more challenging, and the results may not be accurate.
As we plan to improve on this, and as it is unknown when exactly the numerical difficulties arise, we currently only do warn here (in the documentation), but not by giving explicit warning()s.

Author(s)

Diethelm Wuertz for the original Rmetrics R-port. Many numerical improvements by Martin Maechler.

References

Chambers J.M., Mallows, C.L. and Stuck, B.W. (1976) A Method for Simulating Stable Random Variables, J. Amer. Statist. Assoc. 71, 340–344.

John P. Nolan (2012) Stable Distributions - Models for Heavy Tailed Data Birkhauser, Boston; in progress, chapter 1 online at http://academic2.american.edu/~jpnolan/stable/chap1.pdf

Nolan J.P. (1997) Numerical calculation of stable densities and distribution functions. Stochastic Models 13(4), 759–774.
Also available as ‘density.ps’ from Nolan's web page.

Samoridnitsky G., Taqqu M.S. (1994); Stable Non-Gaussian Random Processes, Stochastic Models with Infinite Variance, Chapman and Hall, New York, 632 pages.

Weron, A., Weron R. (1999); Computer Simulation of Levy alpha-Stable Variables and Processes, Preprint Technical University of Wroclaw, 13 pages.

See Also

the stableSlider() function from package fBasics for displaying densities and probabilities of these distributions, for educational purposes.

Examples

## stable -

## Plot stable random number series
   set.seed(1953)
   r <- rstable(n = 1000, alpha = 1.9, beta = 0.3)
   plot(r, type = "l", main = "stable: alpha=1.9 beta=0.3",
        col = "steelblue")
   grid()

## Plot empirical density and compare with true density:
   hist(r, n = 25, probability = TRUE, border = "white",
        col = "steelblue")
   x <- seq(-5, 5, 0.25)
   lines(x, dstable(x, alpha = 1.9, beta = 0.3, tol= 1e-3), lwd = 2)

## Plot df and compare with true df:
   plot(ecdf(r), do.points=TRUE, col = "steelblue",
        main = "Probabilities:  ecdf(rstable(1000,..)) and true  cdf F()")
   rug(r)
   lines(x, pstable(q = x, alpha = 1.9, beta = 0.3),
         col="#0000FF88", lwd= 2.5)

## Switching  sign(beta)  <==> Mirror the distribution around  x == delta:
curve(dstable(x, alpha=1.2, beta =  .8, gamma = 3, delta = 2), -10, 10)
curve(dstable(x, alpha=1.2, beta = -.8, gamma = 3, delta = 2),
      add=TRUE, col=2)
## or the same
curve(dstable(2*2-x, alpha=1.2, beta = +.8, gamma = 3, delta = 2),
      add=TRUE, col=adjustcolor("gray",0.2), lwd=5)
abline(v = 2, col = "gray", lty=2, lwd=2)
axis(1, at = 2, label = expression(delta == 2))

## Compute quantiles:
   x. <- -4:4
   px <- pstable(x., alpha = 1.9, beta = 0.3)
  (qs <- qstable(px, alpha = 1.9, beta = 0.3))
   stopifnot(all.equal(as.vector(qs), x., tol = 1e-5))