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} \left (\frac{s}{p_2}\right )^{p_1-1} \exp \left \{- \left (\frac{s}{p_2}\right )^{p_1} \right \}
with
s \ge 0
wherep_1
is the shape parameter andp_2
the scale parameter.lognorm: The lognormal distribution is implemented as
f(s) = \frac{1}{p_2s\sqrt{2\pi}}e^{-\frac{(log s -p_1)^2}{2p_2^2}}
where
p_1
is the mean andp_2
is the standard deviation of the distirbution.norm: The normal distribution is implemented as
f(s) = \frac{1}{p_2\sqrt{2 \pi}}e^{-\frac{ (s - p_1)^2 }{ 2p_2^2 }}
where
p_1
is the mean and thep_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 \le s \le p_2
.cauchy: The cauchy distribution is implemented as
f(s)=\frac{1}{\pi p_2 \left \{1+( \frac{s-p_1}{p_2})^2\right \} }
where
p_1
is the location parameter andp_2
the scale parameter.fnorm: The half normal distribution is implemented as
2 f(s)-1
where
f(s) = \frac{1}{sd\sqrt{2 \pi} }e^{-\frac{s^2}{2 sd^2 }},
and
sd=\sqrt{\pi/2}/p_1
.normmixt:The normal mixture distribution is implemented as
f(s)=p_1\frac{1}{p_2[2] \sqrt{2\pi} } e^{- \frac{ (s - p_2[1])^2}{2p_2[2]^2}} +(1-p_1)\frac{1}{p_2[4]\sqrt{2\pi}} e^{-\frac{(s - p_2[3])^2}{2p_2[4]^2 }}
where
p1
is a mixture component(scalar) andp_2
a vector of parameters for the mean and variance of the two mixture componentsp_2= c(mean1, sd1, mean2, sd2)
.skewnorm: The skew normal distribution with parameter
p_1
is implemented asf(s)=2\phi(s)\Phi(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}} \left \{ f_t(s/p_1; p_2) I_{\{s \ge 0\}} + f_t(p_1s; p_2)I_{\{s<0 \}}\right \}
where
f_t(x;\nu)
is the p.d.f. of thet
distribution with\nu = 5
degrees of freedom.p_1
controls the skewness of the distribution with values between(0, +\infty)
andp_2
denotes the degrees of freedom.shash: The Sinh-Arcsinh distribution is implemented as
f(s;\mu, p_1, p_2, \tau) = \frac{ce^{-r^2/2}}{\sqrt{2\pi }} \frac{1}{p_2} \frac{1}{2} \sqrt{1+z^2}
where
r=\sinh(\sinh(z)-(-p_1))
,c=\cosh(\sinh(z)-(-p_1))
andz=((s-\mu)/p2)
.p_1
is the vector of skewness,p_2
is the scale parameter,\mu=0
is the location parameter and\tau=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>
References
See Also
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