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.
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14  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 

method 

relativeTolerance 

... 
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 methodrdistq_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 thanrelativeTolerance
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 lengthn
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
withn
rows containing the generated random numbers.
 See Also


sample
Examples
1 2 3 4 5 6 7 8 9 10 11 