Quantiles or empirically based generic random number generation.

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Description

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

The default method generates univariate random numbers specified by arbitrary quantiles.

random.vector generates univariate random numbers drawn from a distribution purely defined empirically.

random.data.frame generates multivariate random numbers drawn from a distribution purely defined empirically.

Usage

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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)

  • 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

  • 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

  • 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

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 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"]])

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