dsn | R Documentation |
Density function, distribution function, quantiles and random number generation for the skew-normal (SN) and the extended skew-normal (ESN) distribution.
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)
x |
vector of quantiles. Missing values ( |
p |
vector of probabilities. Missing values ( |
xi |
vector of location parameters. |
omega |
vector of scale parameters; must be positive. |
alpha |
vector of slant parameter(s); |
tau |
a single value representing the ‘hidden mean’ parameter
of the ESN distribution; |
dp |
a vector of length 3 (in the SN case) or
4 (in the ESN case), whose components represent
the individual parameters described above. If |
n |
a positive integer representing the sample size. |
tol |
a scalar value which regulates the accuracy of the result of
|
log |
logical flag used in |
engine |
a character string which selects the computing engine;
this is either |
solver |
a character string which selects the numerical method used for
solving the quantile equation; possible options are |
... |
additional parameters passed to |
density (dsn
), 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!=0
Typical usages are
dsn(x, xi=0, omega=1, alpha=0, log=FALSE) dsn(x, dp=, log=FALSE) psn(x, xi=0, omega=1, alpha=0, ...) psn(x, dp=, ...) qsn(p, xi=0, omega=1, alpha=0, tol=1e-8, ...) qsn(x, dp=, ...) rsn(n=1, xi=0, omega=1, alpha=0) rsn(x, dp=)
psn
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.
In version 1.6-2, the random number generation method for rsn
has
changed; the so-called transformation method (also referred to as the
‘additive representation’) has been adopted for all values of tau
.
Also, the code has been modified so that there is this form of consistency:
provided set.seed()
is reset similarly before calls, code like
rsn(5, dp=1:3)
and rsn(10, dp=1:3)
, for instance, will start with
the same initial values in the longer sequence as in the shorter sequence.
The family of skew-normal distributions is an extension of the normal
family, via the introdution of a 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 ‘normalized’ case having 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. (1985). A class of distributions which includes the normal ones. Scand. J. Statist. 12, 171-178.
Azzalini, A. with the collaboration of Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.
Functions used by psn
:
T.Owen
, biv.nt.prob
Related distributions: dmsn
, dst
,
dmst
pdf <- 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")
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