| Skellam {skellam} | R Documentation |
The Skellam Distribution
Description
Density, distribution function, quantile function and random number generation for the Skellam distribution.
Usage
dskellam(x, lambda1, lambda2 = lambda1, log = FALSE)
pskellam(q, lambda1, lambda2 = lambda1,
lower.tail = TRUE, log.p = FALSE)
qskellam(p, lambda1, lambda2 = lambda1,
lower.tail = TRUE, log.p = FALSE)
rskellam(n, lambda1, lambda2 = lambda1)
dskellam.sp(x, lambda1, lambda2 = lambda1, log = FALSE)
pskellam.sp(q, lambda1, lambda2 = lambda1,
lower.tail = TRUE, log.p = FALSE)
Arguments
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
lambda1, lambda2 |
vectors of (non-negative) means. |
log, log.p |
logical; if |
lower.tail |
logical; if |
Details
If Y_1 and Y_2 are Poisson variables with means
\mu_1 and \mu_2
and correlation \rho, then X = Y_1 - Y_2 is Skellam with parameters
\lambda_1 = \mu_1 - \rho \sqrt{\mu_1 \mu_2} and
\lambda_2 = \mu_2 - \rho \sqrt{\mu_1 \mu_2}.
dskellam returns a value equivalent to
I(2 \sqrt{\lambda_1 \lambda_2}, |x|)
(\lambda_1/\lambda_2)^{x/2} \exp(-\lambda_1-\lambda_2)
where I(y,\nu) is the modified Bessel function of the first kind.
The |x| differs from most Skellam expressions in the literature, but is correct since x is an integer,
resulting in improved portability and (in R versions prior to 2.9) improved accuracy for x<0.
Exponential scaling is used in besselI to postpone numerical problems.
When numerical problems do occur, a saddlepoint approximation is substituted,
which typically gives at least 4-figure accuracy.
An alternative representation is
dchisq(2 \lambda_1,2(x+1),2\lambda_2) 2
for x \ge 0, and
dchisq(2 \lambda_2,2(1-x),2\lambda_1) 2
for x \le 0; but in R besselI appears to be more accurately implemented
(for very small probabilities) than dchisq.
pskellam(x,lambda1,lambda2) returns
pchisq(2*lambda2, -2*x, 2*lambda1)
for x<=0 and
1 - pchisq(2*lambda1, 2*(x+1), 2*lambda2)
for x>=0.
When pchisq incorrectly returns 0, a saddlepoint approximation is substituted,
which typically gives at least 2-figure accuracy.
The quantile is defined as the smallest value x such that
F(x) \ge p, where F is the distribution function.
For lower.tail=FALSE, the quantile is defined as the largest value x such that
F(x,lower.tail=FALSE) \le p.
rskellam is calculated as rpois(n,lambda1)-rpois(n,lambda2)
dskellam.sp and pskellam.sp return saddlepoint approximations to the pmf and cdf.
They are called by dskellam and pskellam when results from primary methods are in doubt.
Value
dskellam gives the (log) density, pskellam gives the (log) distribution function,
qskellam gives the quantile function, and rskellam generates random deviates.
Invalid lambdas will result in return value NaN, with a warning.
Note
The VGAM package
also contains Skellam functions, which are syntactically similar;
independently developed versions are included here for completeness.
Moreover, this dskellam function offers a broader working range,
correct handling of cases where at least one rate parameter is zero,
enhanced argument checking,
and (in R versions prior to 2.9) improved accuracy for x<0.
If both packages are loaded,
skellam::dskellam
or
VGAM::dskellam
can unambiguously specify which implementation to use.
Author(s)
Jerry W. Lewis, Patrick E. Brown
Source
The relation of dgamma to the modified Bessel function of the first kind
was given by Skellam (1946).
The relation of pgamma to the noncentral chi-square was given by Johnson (1959).
Tables are given by Strackee and van der Gon (1962), which can be used to verify this implementation
(cf. direct calculation in the examples below).
qskellam uses the Cornish–Fisher expansion to include skewness and kurtosis
corrections to a normal approximation, followed by a search.
If getOption("verbose")==TRUE, then qskellam will not use qpois
when one of the lambdas is zero,
in order to verify that this search algorithm has been implemented properly.
References
Butler, R. (2007) Saddlepoint Approximations with Applications, Cambridge University Press, Cambridge & New York, p.17.
Johnson, N. L. (1959) On an extension of the connection between Poisson and \chi^2 distributions.
Biometrika 46, 352–362.
Johnson, N. L.; Kotz, S.; Kemp, A. W. (1993) Univariate Discrete Distributions, 2nd ed., John Wiley and Sons, New York, pp.190-192.
Skellam, J. G. (1946) The frequency distribution of the difference between two Poisson variates belonging to different populations. Journal of the Royal Statistical Society, series A 109/3, 26.
Strackee, J.; van der Gon, J. J. D. (1962) The frequency distribution of the difference between two Poisson variates. Statistica Neerlandica 16/1, 17-23.
Wikipedia. Skellam distribution https://en.wikipedia.org/wiki/Skellam_distribution
Examples
require('skellam')
# one lambda = 0 ~ Poisson
c(dskellam(0:10,5,0), dpois(0:10,5))
c(dskellam(-(0:10),0,5), dpois(0:10,5))
c(pskellam(0:10,5,0,lower.tail=TRUE),
ppois(0:10,5,lower.tail=TRUE))
c(pskellam(0:10,5,0,lower.tail=FALSE),
ppois(0:10,5,lower.tail=FALSE))
c(pskellam(-(0:10),0,5,lower.tail=FALSE),
ppois(0:10-1,5,lower.tail=TRUE))
c(pskellam(-(0:10),0,5,lower.tail=TRUE),
ppois(0:10-1,5,lower.tail=FALSE))
# both lambdas != 0 ~ convolution of Poissons
dskellam(1,0.5,0.75) # sum(dpois(1+0:10,0.5)*dpois(0:10,0.75))
pskellam(1,0.5,0.75) # sum(dskellam(-10:1,0.5,0.75))
dskellam(c(-1,1),c(12,10),c(10,12)) # c(0.0697968,0.0697968)
dskellam(c(-1,1),c(12,10),c(10,12),log=TRUE)
# log(dskellam(c(-1,1),c(12,10),c(10,12)))
dskellam(256,257,1)
# 0.024829348733183769
# exact result for comparison with saddlepoint
dskellam(-3724,2000,3000)
# 3.1058145363400105e-308
# exact result for comparison with saddlepoint
# (still accurate in extreme tail)
pskellam(c(-1,0),c(12,10),c(10,12)) # c(0.2965079,0.7034921)
pskellam(c(-1,0),c(12,10),c(10,12),lower.tail=FALSE)
# 1-pskellam(c(-1,0),c(12,10),c(10,12))
pskellam(-2:2,8.5,10.25,log.p=TRUE) # log(pskellam(-2:2,8.5,10.25))
qskellam(c(0.05,0.95),3,4) # c(-5,3); pskellam(cbind(-6:-5,2:3),3,4)
qskellam(c(0.05,0.95),3,0) # c(1,6); qpois(c(0.05,0.95),3)
rskellam(35,8.5,10.25)