| dsn {sn} | R Documentation |
Density function, distribution function, quantiles and random number generation for the skew-normal (SN) distribution.
dsn(x, location=0, scale=1, shape=0, log=FALSE) dsn(x, dp=, log=FALSE) psn(x, location=0, scale=1, shape=0, ...) psn(x, dp=, log=FALSE) qsn(p, location=0, scale=1, shape=0, tol=1e-8, ...) qsn(x, dp=, log=FALSE) rsn(n=1, location=0, scale=1, shape=0) rsn(x, dp=, log=FALSE)
x |
vector of quantiles. Missing values (NAs) and Inf's
are allowed.
|
p |
vector of probabilities. Missing values (NAs) are allowed.
|
location |
vector of location parameters. |
scale |
vector of (positive) scale parameters. |
shape |
vector of shape parameters.
With psn and qsn, it must be of length 1.
|
dp |
a vector of length 3, whose elements represent location, scale (positive) and
shape, respectively. If dp is specified, this overrides the specification
of the other parameters.
|
n |
sample size. |
tol |
a scalar value which regulates the accuracy of the result of qsn.
|
log |
logical flag used in dsn (default FALSE).
When TRUE, the logarithm of the density values is returned.
|
... |
additional parameters passed to T.Owen
|
density (dsn), probability (psn),
quantile (qsn) or random sample (rsn)
from the skew-normal distribution with given location, scale
and shape parameters.
The family of skew-normal distributions is an extension of the normal
family, via the introdution of a shape parameter which regulates
skewness; when shape=0, the skew-normal distribution reduces to the
normal one. The density of the SN distribution in the "standard" case
having location=0 and scale=1 is
2*dnorm(x)*pnorm(shape*x).
A multivariate version of the distribution exists.
See the references below for additional information.
psn and qsn make use of function T.Owen
Azzalini, A. (1985). A class of distributions which includes the normal ones. Scand. J. Statist. 12, 171-178.
Azzalini, A. and Dalla Valle, A. (1996). The multivariate skew-normal distribution. Biometrika 83, 715–726.
pdf <- dsn(seq(-3,3,by=0.1), shape=3) cdf <- psn(seq(-3,3,by=0.1), shape=3) qu <- qsn(seq(0.1,0.9,by=0.1), shape=-2) rn <- rsn(100, 5, 2, 5)