dsc: Skew-Cauchy Distribution
Description
Density function, distribution function, quantiles and random
number generation for the skew-Cauchy (SC) distribution.Usage
dsc(x, xi = 0, omega = 1, alpha = 0, dp = NULL, log = FALSE)
psc(x, xi = 0, omega = 1, alpha = 0, dp = NULL)
qsc(p, xi = 0, omega = 1, alpha = 0, dp = NULL)
rsc(n = 1, xi = 0, omega = 1, alpha = 0, dp = NULL)
Arguments
x
vector of quantiles. Missing values (NAs) and Inf's
are allowed.
p
vector of probabilities. Missing values (NAs) are allowed.
xi
vector of location parameters.
omega
vector of (positive) scale parameters.
alpha
vector of slant parameters.
dp
a vector of length 3 whose elements represent the parameters
described above. If dp is specified, the individual parameters
cannot be set.
log
logical flag used in dsc (default FALSE).
When TRUE, the logarithm of the density values is returned.
Value
- density (
dsc), probability (psc), quantile (qsc)
or random sample (rsc) from the skew-Cauchy distribution with given
xi, omega and alpha parameters or from the extended
skew-normal if tau!=0
Details
Typical usages are
dsc(x, xi=0, omega=1, alpha=0, log=FALSE)
dsc(x, dp=, log=FALSE)
psc(x, xi=0, omega=1, alpha=0)
psc(x, dp= )
qsc(p, xi=0, omega=1, alpha=0)
qsc(x, dp=)
rsc(n=1, xi=0, omega=1, alpha=0)
rsc(x, dp=)Background
The skew-Cauchy distribution can be thought as a skew-$t$ with tail-weight
parameter nu=1. In this case, closed-form expressions of the
distribution function and the quantile function have been obtained by
Behboodian et al. (2006).
The key facts are summarized in Complement 4.2 of Azzalini and Capitanio (2014).
A multivariate version of the distribution exists.References
Azzalini, A. with the collaboration of Capitanio, A. (2014).
The Skew-normal and Related Families.
Cambridge University Press, IMS Monographs series.Behboodian, J., Jamalizadeh, A., and Balakrishnan, N. (2006).
A new class of skew-Cauchy distributions.
Statist. Probab. Lett. 76, 1488--1493.
Examples
Run this codepdf <- dsc(seq(-5,5,by=0.1), alpha=3)
cdf <- psc(seq(-5,5,by=0.1), alpha=3)
q <- qsc(seq(0.1,0.9,by=0.1), alpha=-2)
p <- psc(q, alpha=-2)
rn <- rsc(100, 5, 2, 5)
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