r.sample {asymmetry.measures} R Documentation

## Switch between a range of available random number generators.

### Description

Generate a random sample of size n out of a range of available distributions.

### Usage

r.sample(s, dist, p1=0, p2=1)

### Arguments

 s A scalar which specifies the size of the random sample drawn. dist Character string, used as a switch to the user selected distribution function (see details below). p1 A scalar. Parameter 1 (vector or object) of the selected distribution. p2 A scalar. Parameter 2 (vector or object) of the selected distribution.

### Details

Based on user-specified argument dist, the function returns a random sample of size s from the corresponding distribution.

Supported distributions (along with the corresponding dist values) are:

• weib: The weibull distribution is implemented as

f(s;p_1,p_2)= \frac{p_1}{p_2} ≤ft (\frac{s}{p_2}\right )^{p_1-1} \exp ≤ft \{- ≤ft (\frac{s}{p_2}\right )^{p_1} \right \}

with s ≥ 0 where p_1 is the shape parameter and p_2 the scale parameter.

• lognorm: The lognormal distribution is implemented as

f(s) = \frac{1}{p_2s√{2π}}e^{-\frac{(log s -p_1)^2}{2p_2^2}}

where p_1 is the mean and p_2 is the standard deviation of the distirbution.

• norm: The normal distribution is implemented as

f(s) = \frac{1}{p_2√{2 π}}e^{-\frac{ (s - p_1)^2 }{ 2p_2^2 }}

where p_1 is the mean and the p_2 is the standard deviation of the distirbution.

• uni: The uniform distribution is implemented as

f(s) = \frac{1}{p_2-p_1}

for p_1 ≤ s ≤ p_2.

• cauchy: The cauchy distribution is implemented as

f(s)=\frac{1}{π p_2 ≤ft \{1+( \frac{s-p_1}{p_2})^2\right \} }

where p_1 is the location parameter and p_2 the scale parameter.

• fnorm: The half normal distribution is implemented as

2 f(s)-1

where

f(s) = \frac{1}{sd√{2 π} }e^{-\frac{s^2}{2 sd^2 }},

and sd=√{π/2}/p_1.

• normmixt:The normal mixture distribution is implemented as

f(s)=p_1\frac{1}{p_2 √{2π} } e^{- \frac{ (s - p_2)^2}{2p_2^2}} +(1-p_1)\frac{1}{p_2√{2π}} e^{-\frac{(s - p_2)^2}{2p_2^2 }}

where p1 is a mixture component(scalar) and p_2 a vector of parameters for the mean and variance of the two mixture components p_2= c(mean1, sd1, mean2, sd2).

• skewnorm: The skew normal distribution with parameter p_1 is implemented as

f(s)=2φ(s)Φ(p_1s)

.

• fas: The Fernandez and Steel distribution is implemented as

f(s; p_1, p_2) = \frac{2}{p_1+\frac{1}{p_1}} ≤ft \{ f_t(s/p_1; p_2) I_{\{s ≥ 0\}} + f_t(p_1s; p_2)I_{\{s<0 \}}\right \}

where f_t(x;ν) is the p.d.f. of the t distribution with ν = 5 degrees of freedom. p_1 controls the skewness of the distribution with values between (0, +∞) and p_2 denotes the degrees of freedom.

• shash: The Sinh-Arcsinh distribution is implemented as

f(s;μ, p_1, p_2, τ) = \frac{ce^{-r^2/2}}{√{2π }} \frac{1}{p_2} \frac{1}{2} √{1+z^2}

where r=\sinh(\sinh(z)-(-p_1)), c=\cosh(\sinh(z)-(-p_1)) and z=((s-μ)/p2). p_1 is the vector of skewness, p_2 is the scale parameter, μ=0 is the location parameter and τ=1 the kurtosis parameter.

### Value

A vector of random values at the user specified points s.

### Author(s)

Dimitrios Bagkavos and Lucia Gamez Gallardo

R implementation and documentation: Dimitrios Bagkavos <dimitrios.bagkavos@gmail.com> , Lucia Gamez Gallardo <gamezgallardolucia@gmail.com>

### See Also

d.sample, q.sample, p.sample

### Examples

selected.r <- "norm" #select Normal as the distribution
shape <- 2  # specify shape parameter
scale <- 1  # specify scale parameter
n <- 100    # specify sample size
r.sample(n,selected.r,shape,scale)  # calculate CDF at the designated point


[Package asymmetry.measures version 0.2 Index]