Geometric Distribution

DESCRIPTION:

Functions for the density, cumulative distribution, quantiles and random generation of the Geometric distribution. The distribution models the number of failures before the first success in a sequence of Bernoulli trials.

USAGE:

dgeom(x, prob, log=F) 
pgeom(q, prob) 
qgeom(p, prob) 
rgeom(n, prob, bigdata=F
) 

REQUIRED ARGUMENTS:

x
vector or bdVector of (positive) quantiles. Missing values ( NAs) are allowed.
q
vector or bdVector of (positive) quantiles. Missing values ( NAs) are allowed.
p
vector or bdVector of probabilities. Missing values ( NAs) are allowed.
n
sample size. If length(n) is larger than 1, then length(n) random values are returned; otherwise n random values are returned.
prob
vector or bdVector of probability parameters between 0 and 1.

OPTIONAL ARGUMENTS:

bigdata
a logical value; if TRUE, an object of type bdVector is returned. Otherwise, a vector object is returned. This argument can be used only if the bigdata library section has been loaded.
log
a logical scalar; if TRUE, dgeom will return the log of the density, not the density itself.

VALUE:

density ( dgeom), probability ( pgeom), quantile ( qgeom), or random sample ( rgeom) for the Geometric distribution with parameter prob. The quantile is defined as the smallest value q such that Pr(Geometric random variate <= x) >= p.

SIDE EFFECTS:

rgeom causes the creation of the dataset .Random.seed if it does not already exist, otherwise its value is updated.

DETAILS:

Elements of q or p that are missing will cause the corresponding elements of the result to be missing.

For details on the uniform random number generator implemented in S-PLUS, see the set.seed help file.

SEE ALSO:

, , .

EXAMPLES:

rgeom(20, 0.6)  #sample of size 20 with parameter 0.6