random {decisionSupport}

Quantiles or empirically based generic random number generation.

Description

These functions generate random numbers for parametric distributions, parameters of which are determined by given quantiles or for distributions purely defined empirically.

Usage

random(rho, n, method, relativeTolerance, ...)

## Default S3 method:
random(
  rho = list(distribution = "norm", probabilities = c(0.05, 0.95), quantiles =
    c(-qnorm(0.95), qnorm(0.95))),
  n,
  method = "fit",
  relativeTolerance = 0.05,
  ...
)

## S3 method for class 'vector'
random(rho = runif(n = n), n, method = NULL, relativeTolerance = NULL, ...)

## S3 method for class 'data.frame'
random(
  rho = data.frame(uniform = runif(n = n)),
  n,
  method = NULL,
  relativeTolerance = NULL,
  ...
)

Arguments

rho

Distribution to be randomly sampled.

n

integer: Number of observations to be generated

method

character: Particular method to be used for random number generation.

relativeTolerance

numeric: the relative tolerance level of deviation of the generated confidence interval from the specified interval. If this deviation is greater than relativeTolerance a warning is given.

...

Optional arguments to be passed to the particular random number generating function.

Methods (by class)

• random(default): Quantiles based univariate random number generation.

  Arguments

  rho

  rho list: Distribution to be randomly sampled. The list elements are $distribution, $probabilities and $quantiles. For details cf. below.

  method

  character: Particular method to be used for random number generation. Currently only method rdistq_fit{fit} is implemented which is the default.

  relativeTolerance

  numeric: the relative tolerance level of deviation of the generated confidence interval from the specified interval. If this deviation is greater than relativeTolerance a warning is given.

  ...

  Optional arguments to be passed to the particular random number generating function, i.e. rdistq_fit.

  Details

  The distribution family is determined by rho[["distribution"]]. For the possibilities cf. rdistq_fit.

  rho[["probabilities"]] and [[rho"quantiles"]] are numeric vectors of the same length. The first defines the probabilities of the quantiles, the second defines the quantiles values which determine the parametric distribution.

  Value

  A numeric vector of length n containing the generated random numbers.

  See Also

  rdistq_fit

• random(vector): Univariate random number generation by drawing from a given empirical sample.

  Arguments

  rho

  vector: Univariate empirical sample to be sampled from.

  method

  for this class no impact

  relativeTolerance

  for this class no impact

  ...

  for this class no impact

  Value

  A numeric vector of length n containing the generated random numbers.

  See Also

  sample

• random(data.frame): Multivariate random number generation by drawing from a given empirical sample.

  Arguments

  rho

  data.frame: Multivariate empirical sample to be sampled from.

  method

  for this class no impact

  relativeTolerance

  for this class no impact

  ...

  for this class no impact

  Value

  A data.frame with n rows containing the generated random numbers.

  See Also

  sample

Examples

 x<-random(n=10000)
 hist(x,breaks=100)
 mean(x)
 sd(x)
  
 rho<-list(distribution="norm", 
           probabilities=c(0.05,0.4,0.8), 
           quantiles=c(-4, 20, 100))
 x<-random(rho=rho, n=10000, tolConv=0.01)
 hist(x,breaks=100)
 quantile(x,p=rho[["probabilities"]])