pdf.HyperGeometric: Evaluate the probability mass function of a HyperGeometric...

In distributions3: Probability Distributions as S3 Objects

Evaluate the probability mass function of a HyperGeometric distribution

Description

Please see the documentation of HyperGeometric() for some properties of the HyperGeometric distribution, as well as extensive examples showing to how calculate p-values and confidence intervals.

Usage

## S3 method for class 'HyperGeometric'
pdf(d, x, drop = TRUE, elementwise = NULL, ...)

## S3 method for class 'HyperGeometric'
log_pdf(d, x, drop = TRUE, elementwise = NULL, ...)

Arguments

d	A HyperGeometric object created by a call to HyperGeometric().
x	A vector of elements whose probabilities you would like to determine given the distribution d.
drop	logical. Should the result be simplified to a vector if possible?
elementwise	logical. Should each distribution in d be evaluated at all elements of x (elementwise = FALSE, yielding a matrix)? Or, if d and x have the same length, should the evaluation be done element by element (elementwise = TRUE, yielding a vector)? The default of NULL means that elementwise = TRUE is used if the lengths match and otherwise elementwise = FALSE is used.
...	Arguments to be passed to dhyper. Unevaluated arguments will generate a warning to catch mispellings or other possible errors.

Value

In case of a single distribution object, either a numeric vector of length probs (if drop = TRUE, default) or a matrix with length(x) columns (if drop = FALSE). In case of a vectorized distribution object, a matrix with length(x) columns containing all possible combinations.

See Also

Other HyperGeometric distribution: cdf.HyperGeometric(), quantile.HyperGeometric(), random.HyperGeometric()

Examples

set.seed(27)

X <- HyperGeometric(4, 5, 8)
X

random(X, 10)

pdf(X, 2)
log_pdf(X, 2)

cdf(X, 4)
quantile(X, 0.7)

distributions3 documentation built on Sept. 30, 2024, 9:37 a.m.