Evaluate the probability mass function of a HyperGeometric distribution

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
#> [1] "HyperGeometric(m = 4, n = 5, k = 8)"

random(X, 10)
#>  [1] 3 4 3 4 4 4 4 4 4 4

pdf(X, 2)
#> [1] 0
log_pdf(X, 2)
#> [1] -Inf

cdf(X, 4)
#> [1] 1
quantile(X, 0.7)
#> [1] 4