dsn(x, xi=0, omega=1, alpha=0, tau=0, dp=NULL, log=FALSE)
psn(x, xi=0, omega=1, alpha=0, tau=0, dp=NULL, engine, ...)
qsn(p, xi=0, omega=1, alpha=0, tau=0, dp=NULL, tol=1e-8, solver="NR", ...)
rsn(n=1, xi=0, omega=1, alpha=0, tau=0, dp=NULL)NA's) and Inf's
are allowed.NAs) are allowed+/- Inf is allowed.
With psn and qsn, it must be of length 1 if
engine="T.Owen".tau=0 (default) corresponds to
a SN distribution.dp
is specified, the individual parameters cannot be set.qsn, measured on the probability scale.dsn (default FALSE).
When TRUE, the logarithm of the density values is returned."T.Owen" or "biv.nt.prob", the latter from
package mnormt. If tau != 0 or length(alpha)>1,
"biv.nt.pro"NR" (default)
and "RFB", described in the T.Owendsn), probability (psn), quantile (qsn)
or random sample (rsn) from the skew-normal distribution with given
xi, omega and alpha parameters or from the extended
skew-normal if tau!=0psn and qsn make use of function T.Owen
or biv.nt.prob
In qsn, the choice solver="NR" selects the Newton-Raphson method
for solving the quantile equation, while option solver="RFB"
alternates a step of regula falsi with one of bisection.
The "NR" method is generally more efficient, but "RFB" is
occasionally required in some problematic cases.alpha parameter which regulates
asymmetry; when alpha=0, the skew-normal distribution reduces to
the normal one. The density function of the SN distribution
in the xi=0 and omega=1 is
$2\phi(x)\Phi(\alpha x)$, if $\phi$ and $\Phi$ denote the
standard normal density and distribution function.
An early discussion of the skew-normal distribution is given by
Azzalini (1985); see Section 3.3 for the ESN variant,
up to a slight difference in the parameterization.An updated exposition is provided in Chapter 2 of Azzalini and Capitanio (2014); the ESN variant is presented Section 2.2. See Section 2.3 for an historical account. A multivariate version of the distribution is examined in Chapter 5.
Azzalini, A. with the collaboration of Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.
psn:
T.Owen, biv.nt.prob
Related distributions: dmsn, dst,
dmstpdf <- dsn(seq(-3, 3, by=0.1), alpha=3)
cdf <- psn(seq(-3, 3, by=0.1), alpha=3)
q <- qsn(seq(0.1, 0.9, by=0.1), alpha=-2)
r <- rsn(100, 5, 2, 5)
qsn(1/10^(1:4), 0, 1, 5, 3, solver="RFB")Run the code above in your browser using DataLab