Draw a random sample from a probability distribution

Generic function for drawing random samples from distribution objects.

Usage

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

# S3 method for class 'distribution'
simulate(object, nsim = 1L, seed = NULL, ...)

Arguments

x, object: An object. The package provides methods for distribution objects such as those from Normal() or Binomial() etc.
n, nsim: The number of samples to draw. Should be a positive integer. Defaults to 1L.
drop: logical. Should the result be simplified to a vector if possible?
...: Arguments passed to methods. Unevaluated arguments will generate a warning to catch mispellings or other possible errors.
seed: An optional random seed that is to be set using set.seed prior to drawing the random sample. The previous random seed from the global environment (if any) is restored afterwards.

Value

Random samples drawn from the distriubtion x. The random methods typically return either a matrix or, if possible, a vector. The simulate method always returns a data frame (with an attribute "seed" containing the .Random.seed from before the simulation).

Details

random is a new generic for drawing random samples from the S3 distribution objects provided in this package, such as Normal or Binomial etc. The respective methods typically call the "r" function for the corresponding distribution functions provided in base R such as rnorm, rbinom etc.

In addition to the new random generic there is also a simulate method for distribution objects which simply calls the random method internally.

Examples

## distribution object
X <- Normal()
## 10 random samples
random(X, 10)
#>  [1]  0.295241218  0.006885942  1.157410886  2.134637891  0.237844613
#>  [6] -1.285127357  0.034827247  1.570295342  0.158010051 -0.745799472