View source: R/Distributions.R
dmesn | R Documentation |
Density function and distribution function for the bivariate and trivariate extended skew-normal (ESN) distribution.
dmesn(
x = c(0, 0),
location = rep(0, length(x)),
scale = diag(length(x)),
shape = rep(0, length(x)),
extended = 0
)
pmesn(
x = c(0, 0),
location = rep(0, length(x)),
scale = diag(length(x)),
shape = rep(0, length(x)),
extended = 0
)
x |
Quantile vector of length |
location |
A numeric location vector of length |
scale |
A symmetric positive-definite scale matrix of dimension |
shape |
A numeric skewness vector of length |
extended |
A single extension parameter. Default is |
dmesn
returns the density, and pmesn
returns the probability,
of the bivariate or trivariate extended skew-normal distribution with the
specified location
, scale
, shape
, and extended
parameters.
If extended = 0
, the skew-normal distribution is obtained.
If shape = 0
and extended = 0
, the normal distribution is recovered.
Simone Padoan, simone.padoan@unibocconi.it, https://faculty.unibocconi.it/simonepadoan/; Boris Beranger, borisberanger@gmail.com, https://www.borisberanger.com.
Azzalini, A. and Capitanio, A. (1999). Statistical applications of the multivariate skew normal distribution. J. Roy. Statist. Soc. B 61, 579–602.
Azzalini, A. with the collaboration of Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.
Azzalini, A. and Dalla Valle, A. (1996). The multivariate skew-normal distribution. Biometrika 83, 715–726.
sigma1 <- matrix(c(2, 1.5, 1.5, 3), ncol = 2)
sigma2 <- matrix(c(
2, 1.5, 1.8,
1.5, 3, 2.2,
1.8, 2.2, 3.5
), ncol = 3)
shape1 <- c(1, 2)
shape2 <- c(1, 2, 1.5)
dens1 <- dmesn(x = c(1, 1), scale = sigma1, shape = shape1, extended = 2)
dens2 <- dmesn(x = c(1, 1), scale = sigma1)
dens3 <- dmesn(x = c(1, 1, 1), scale = sigma2, shape = shape2, extended = 2)
dens4 <- dmesn(x = c(1, 1, 1), scale = sigma2)
prob1 <- pmesn(x = c(1, 1), scale = sigma1, shape = shape1, extended = 2)
prob2 <- pmesn(x = c(1, 1), scale = sigma1)
prob3 <- pmesn(x = c(1, 1, 1), scale = sigma2, shape = shape2, extended = 2)
prob4 <- pmesn(x = c(1, 1, 1), scale = sigma2)
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