dbinom(x, size, prob, log=F) pbinom(q, size, prob) qbinom(p, size, prob) rbinom(n, size, prob, bigdata=F)
bdVector
of quantiles.
Missing values (
NA
s) are allowed.
bdVector
of (positive) quantiles (number of successes obtained in
size
binomial trials with probability
prob
of success).
Missing values (
NA
s) are allowed.
bdVector
of probabilities.
Missing values (
NA
s) are allowed.
bdVector
of (positive integer) numbers of coin flips for which
the Binomial distribution measures the number of heads.
bdVector
of probabilities of a head.
If
length(n)
is larger than 1, then
length(n)
random values are returned.
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
,
dbinom
will return
the log of the density, not the density itself.
dbinom
),
probability (
pbinom
),
quantile (
qbinom
), or
random sample (
rbinom
)
for the Binomial distribution with parameters
size
and
prob
.
The quantile is defined as the smallest value
q
such that Pr(Binomial random
variate <=
q
) >=
p
.
rbinom
causes the 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.
A Binomial discrete random variable X is the number of
successes in
n
independent repetitions of a simple success-failure experiment
where
p
is the probability of success. For example, consider the
experiment of tossing a coin
n
times where the probability of
the coin landing heads is
p
. A special case is the
Bernoulli trial when
n == 1
(a coin toss).
For details on the uniform random number generator implemented in S-PLUS,
see the
set.seed
help file.
Hoel, P., Port, S. and Stone, C. (1971).
Introduction to Probability Theory.
Houghton-Mifflin, Boston, MA.
Johnson, N. L. and Kotz, S. (1970).
Discrete Univariate Distributions, vol. 2.
Houghton-Mifflin, Boston, MA.
rbinom(20, 10, 0.5) # sample of size 20 with mean 10*0.5 = 5 rbinom(11, 10, 0:10/10) # different values of prob rbinom(10, 1:10, .5) # different values of size