R/comp_distribution.R
COM_Poisson_Distribution.Rd
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
)
vector of quantiles
mean and dispersion parameters. Must be strictly positive.
an alternative way than mu to parametrized the distribution. Must be strictly positive
logical; if TRUE
, probabilities/densities \(p\) are returned as
\(log(p)\).
numeric: the lower and upper end points for the interval to be searched for lambda(s).
numeric: the maximum number of iterations allowed to solve for lambda(s).
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.
numeric; maximum number of terms to be considered in the truncated sum.
logical; if TRUE
(default), probabilities are \(P(X \le x)\),
otherwise, \(P(X>x)\).
vector of probabilities
number of observations. If length(n)
> 1, the length is taken to
be the number required.
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)
#> [1] 0.11374731 0.27609781 0.29170831 0.18946331 0.08713145 0.03065722
pcomp(5, mu = 2, nu = 1.2)
#> [1] 0.9888054
p <- (1:9) / 10
qcomp(p, mu = 2, nu = 0.8)
#> [1] 0 1 1 1 2 2 3 3 4
rcomp(10, mu = 2, nu = 0.7)
#> [1] 3 0 2 5 7 1 0 4 7 7