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.
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, ...)

rho 
Distribution to be randomly sampled. 
n 

method 

relativeTolerance 

... 
Optional arguments to be passed to the particular random number generating function. 
default
: Quantiles based univariate random number generation.
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
.
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.
A numeric vector of length n
containing the generated random numbers.
rdistq_fit
vector
: Univariate random number generation by drawing from a given
empirical sample.
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
A numeric vector
of length n
containing the generated random numbers.
sample
data.frame
: Multivariate random number generation by drawing from a given empirical sample.
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
A data.frame
with n
rows containing the generated random numbers.
sample
1 2 3 4 5 6 7 8 9 10 11 
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