Skellam {skellam} | R Documentation |
Density, distribution function, quantile function and random number
generation for the Skellam distribution with parameters lambda1
and lambda2
.
If Y1 and Y2 are Poisson variables with means mu1 and mu2
and correlation rho, then X = Y1 - Y2 is Skellam with parameters
lambda1 = mu1 - rho*sqrt(mu1*mu2) and lambda2 = mu2 - rho*sqrt(mu1*mu2).
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)
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. |
lambda1, lambda2 |
vectors of (non-negative) means. |
log, log.p |
logical; if TRUE , probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are P[X <= x],
otherwise, P[X > x]. |
dskellam
returns a value equivalent to
I(2 sqrt(lambda1 lambda2), abs(x)) (lambda1/lambda2)^(x/2) exp(-lambda1-lambda2)
where I(y,nu) is the modified Bessel function of the first kind.
The abs(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*lambda1,2*(x+1),2*lambda2)*2
for x >= 0, and
dchisq(2*lambda2,2*(1-x),2*lambda1)*2
for x <= 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) >= 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
) <= 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.
dskellam
gives the (log) density, pskellam
gives the (log) distribution function,
qskellam
gives the quantile function, and rskellam
generates random deviates.
Invalid lambda
s will result in return value NaN
, with a warning.
The VGAM
package
(http://lib.stat.cmu.edu/R/CRAN/web/packages/VGAM/index.html)
also contains dskellam
and rskellam
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,
get("dskellam",pos="package:skellam")
or
get("dskellam",pos="package:VGAM")
can unambiguously specify which implementation to use.
Jerry W. Lewis
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 lambda
s is zero,
in order to verify that this search algorithm has been implemented properly.
Butler, R. (2007) Saddlepoint Approximations with Applications, Cambridge University Press, Cambridge & New York, p.17.
Johnson, N. L. (1959) On an extension of the connexion 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 http://en.wikipedia.org/wiki/Skellam_distribution
require(skellam) # one lambda = 0 ~ Poisson dskellam(0:10,5,0) # dpois(0:10,5) dskellam(-(0:10),0,5) # dpois(0:10,5) pskellam(0:10,5,0,lower.tail=TRUE) # ppois(0:10,5,lower.tail=TRUE) pskellam(0:10,5,0,lower.tail=FALSE) # ppois(0:10,5,lower.tail=FALSE) pskellam(-(0:10),0,5,lower.tail=FALSE) # ppois(0:10-1,5,lower.tail=TRUE) 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)