dzinbinom | R Documentation |
Density, distribution function, quantile function, and random
generation for the zero-inflated negative binomial distribution with
parameters mu
, theta
(or size
), and pi
.
dzinbinom(x, mu, theta, size, pi, log = FALSE) pzinbinom(q, mu, theta, size, pi, lower.tail = TRUE, log.p = FALSE) qzinbinom(p, mu, theta, size, pi, lower.tail = TRUE, log.p = FALSE) rzinbinom(n, mu, theta, size, pi)
x |
vector of (non-negative integer) quantiles. |
mu |
vector of (non-negative) negative binomial location parameters. |
theta, size |
vector of (non-negative) negative binomial overdispersion parameters.
Only |
pi |
vector of zero-inflation probabilities in the unit interval. |
log, log.p |
logical indicating whether probabilities p are given as log(p). |
q |
vector of quantiles. |
lower.tail |
logical indicating whether probabilities are P[X ≤ x] (lower tail) or P[X > x] (upper tail). |
p |
vector of probabilities. |
n |
number of random values to return. |
All functions follow the usual conventions of d/p/q/r functions
in base R. In particular, all four zinbinom
functions for the
zero-inflated negative binomial distribution call the corresponding nbinom
functions for the negative binomial distribution from base R internally.
Note, however, that the precision of qzinbinom
for very large
probabilities (close to 1) is limited because the probabilities
are internally handled in levels and not in logs (even if log.p = TRUE
).
ZINegativeBinomial
, dnbinom
## theoretical probabilities for a zero-inflated negative binomial distribution x <- 0:8 p <- dzinbinom(x, mu = 2.5, theta = 1, pi = 0.25) plot(x, p, type = "h", lwd = 2) ## corresponding empirical frequencies from a simulated sample set.seed(0) y <- rzinbinom(500, mu = 2.5, theta = 1, pi = 0.25) hist(y, breaks = -1:max(y) + 0.5)
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