rdistq_fit: Quantiles based univariate random number generation (by parameter fitting).

Description

This function generates random numbers for a set of univariate parametric distributions from given quantiles. Internally, this is achieved by fitting the distribution function to the given quantiles.

Usage

rdistq_fit(distribution, n, percentiles = c(0.05, 0.5, 0.95), quantiles,
  relativeTolerance = 0.05, tolConv = 0.001, fit.weights = rep(1,
  length(percentiles)), verbosity = 1)

Arguments

distribution

A character string that defines the univariate distribution to be randomly sampled.

Number of generated observations.

percentiles

Numeric vector giving the percentiles.

quantiles

Numeric vector giving the quantiles.

relativeTolerance

numeric; the relative tolerance level of deviation of the generated individual percentiles from the specified percentiles. If any deviation is greater than relativeTolerance a warning is given.

tolConv

positive numerical value, the absolute convergence tolerance for reaching zero by fitting distributions get.norm.par will be shown.

fit.weights

numerical vector of the same length as a probabilities vector p containing positive values for weighting quantiles. By default all quantiles will be weighted by 1.

verbosity

integer; if 0 the function is silent; the larger the value the more verbose is the output information.

Value

A numeric vector of length n with the sampled values according to the chosen distribution.

Details

The following table shows the available distributions and their identification (option: distribution) as a character string:

`distribution`	Distribution Name	`length(quantiles)`	Necessary Package
`"norm"`	Normal	>=2
`"beta"`	Beta	>=2
`"cauchy"`	Cauchy	>=2
`"logis"`	Logistic	>=2
`"t"`	Student t	>=1
`"chisq"`	Central Chi-Squared	>=1
`"chisqnc"`	Non-central Chi-Squared	>=2
`"exp"`	Exponential	>=1
`"f"`	Central F	>=2
`"gamma"`	Gamma with `scale=1/rate`	>=2
`"lnorm"`	Log Normal	>=2
`"unif"`	Uniform	==2
`"weibull"`	Weibull	>=2
`"triang"`	Triangular	>=3	`mc2d`
`"gompertz"`	Gompertz	>=2	`eha`
`"pert"`	(Modified) PERT	>=4	`mc2d`

percentiles and quantiles must be of the same length. percentiles must be >=0 and <=1.

The default for percentiles is 0.05, 0.5 and 0.95, so for the default, the quantiles argument should be a vector with 3 elements. If this is to be longer, the percentiles argument has to be adjusted to match the length of quantiles.

The fitting of the distribution parameters is done using rriskFitdist.perc.

Examples

Run this code

# NOT RUN {
# Fit a log normal distribution to 3 quantiles:
if ( requireNamespace("rriskDistributions", quietly = TRUE) ){
  percentiles<-c(0.05, 0.5, 0.95)
  quantiles=c(1,3,15)
  hist(r<-rdistq_fit(distribution="lnorm", n=10000, quantiles=quantiles),breaks=100)
  print(quantile(x=r, probs=percentiles))
}
# }