COM_Poisson_Distribution | R Documentation |
Density, distribution function, quantile function and random generation for the
Conway-Maxwell-Poisson distribution with parameter mu
and nu
dcomp( x, mu, nu = 1, lambda, log.p = FALSE, lambdalb = 1e-10, lambdaub = 1000, maxlambdaiter = 1000, tol = 1e-06, summax ) pcomp( q, mu, nu = 1, lambda, lower.tail = TRUE, log.p = FALSE, lambdalb = 1e-10, lambdaub = 1000, maxlambdaiter = 1000, tol = 1e-06, summax ) qcomp( p, mu, nu = 1, lambda, lower.tail = TRUE, log.p = FALSE, lambdalb = 1e-10, lambdaub = 1000, maxlambdaiter = 1000, tol = 1e-06, summax ) rcomp( n, mu, nu = 1, lambda, lambdalb = 1e-10, lambdaub = 1000, maxlambdaiter = 1000, tol = 1e-06, summax )
x, q |
vector of quantiles |
mu, nu |
mean and dispersion parameters. Must be strictly positive. |
lambda |
an alternative way than mu to parametrized the distribution. Must be strictly positive |
log.p |
logical; if |
lambdalb, lambdaub |
numeric: the lower and upper end points for the interval to be searched for lambda(s). |
maxlambdaiter |
numeric: the maximum number of iterations allowed to solve for lambda(s). |
tol |
numeric: the convergence threshold. A lambda is said to satisfy the mean constraint if the absolute difference between the calculated mean and mu is less than tol. |
summax |
numeric; maximum number of terms to be considered in the truncated sum. |
lower.tail |
logical; if |
p |
vector of probabilities |
n |
number of observations. If |
dcomp
gives the density, pcomp
gives the distribution function, qcomp
gives the quantile function, and rcomp
generates random deviates.
Invalid arguments will result in return value NaN
, with a warning.
The length of the results is determined by n
for rcomp
, 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 results.
Only the first argument of the logical arguments are used.
dcomp(0:5, mu = 2, nu = 1.2) pcomp(5, mu = 2, nu = 1.2) p <- (1:9) / 10 qcomp(p, mu = 2, nu = 0.8) rcomp(10, mu = 2, nu = 0.7)
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