prob.
dgeom(x, prob, log = FALSE)
pgeom(q, prob, lower.tail = TRUE, log.p = FALSE)
qgeom(p, prob, lower.tail = TRUE, log.p = FALSE)
rgeom(n, prob)length(n) > 1, the length
is taken to be the number required.0 < prob <= 1<="" code="">.dgeom gives the density,
pgeom gives the distribution function,
qgeom gives the quantile function, and
rgeom generates random deviates.Invalid prob will result in return value NaN, with a warning.The length of the result is determined by n for
rgeom, 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 result. Only the first elements of the logical
arguments are used.
dgeom computes via dbinom, using code contributed by
Catherine Loader (see dbinom). pgeom and qgeom are based on the closed-form formulae. rgeom uses the derivation as an exponential mixture of Poissons, see Devroye, L. (1986) Non-Uniform Random Variate Generation.
Springer-Verlag, New York. Page 480.prob $= p$ has density
$$p(x) = p {(1-p)}^{x}$$
for $x = 0, 1, 2, \ldots$, $0 < p \le 1$. If an element of x is not integer, the result of dgeom
is zero, with a warning.
The quantile is defined as the smallest value $x$ such that $F(x) \ge p$, where $F$ is the distribution function.
dnbinom for the negative binomial which generalizes
the geometric distribution.
qgeom((1:9)/10, prob = .2)
Ni <- rgeom(20, prob = 1/4); table(factor(Ni, 0:max(Ni)))
Run the code above in your browser using DataLab