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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 HyperGeometric
random(x, n = 1L, drop = TRUE, ...)

Arguments

x

A HyperGeometric object created by a call to HyperGeometric().

n

The number of samples to draw. Defaults to 1L.

drop

logical. Should the result be simplified to a vector if possible?

...

Unused. Unevaluated arguments will generate a warning to catch mispellings or other possible errors.

Value

In case of a single distribution object or n = 1, either a numeric vector of length n (if drop = TRUE, default) or a matrix with n columns (if drop = FALSE).

See also

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

Examples


set.seed(27)

X <- HyperGeometric(4, 5, 8)
X
#> [1] "HyperGeometric distribution (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