bdVectors.
dbeta(x, shape1, shape2, log=F) pbeta(q, shape1, shape2, ncp=0, lower.tail=TRUE, log.p=FALSE) qbeta(p, shape1, shape2) rbeta(n, shape1, shape2, bigdata=F)
bdVector of quantiles.
Missing values (
NAs) are allowed.
bdVector of quantiles.
Missing values (
NAs) are allowed.
bdVector of probabilities.
Missing values (
NAs) are allowed.
length(n) is larger than 1, then
length(n) random values are returned.
bdVector of (positive) shape parameters.
This is replicated to be the same length as
p or
q or the number of
deviates generated.
bdVector of (positive) shape parameters.
This is replicated to be the same length as
p or
q or the number of
deviates generated.
bdVector of non-negative noncentrality parameters.
See Posten (1993) for a description of the parameterization
(it corresponds to that for
Chisquare).
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.
TRUE,
dbeta will return
the log of the density, not the density itself.
P[X <= x], otherwise,
P[X > x].
TRUE, probabilities
p are given as
log(p). Default is
FALSE.
dbeta),
probability (
pbeta),
quantile (
qbeta), or
random sample (
rbeta)
for the standard beta distribution with parameters
shape1 and
shape2.
rbeta causes creation of the dataset
.Random.seed if it does
not already exist, otherwise its value is updated.
Elements of
q or
p that are missing will cause the corresponding
elements of the result to be missing.
The algorithm for computing noncentrality in
pbeta is described in
Posten (1993).
The beta distribution takes real values between 0 and 1. Special cases of
the beta are the Uniform[0,1] (see
Uniform)
and the arcsin distribution when
shape1 =
shape2 = .5. The arcsin distribution appears
in the theory of random walks. The beta distribution is used in Bayesian
analyses as a conjugate to the binomial distribution.
For details on the uniform random number generator implemented in S-PLUS,
see the
set.seed help file.
Johnson, N. L. and Kotz, S. (1970).
Continuous Univariate Distributions, vol. 2.
Houghton-Mifflin, Boston.
Posten, H. O. (1993). An effective algorithm for the noncentral beta
distribution function.
The American Statistician
47 129-131.
rbeta(20,2,3) #sample of 20 with shape parameters 2 and 3