dprmvSN | R Documentation |
These functions provide the density function and a random number
generator for the multivariate skew normal (SN) distribution with mean vector mu
, scale matrix Sigma
and skewness parameter lambda
.
dmvSN(x,mu=rep(0,length(lambda)),Sigma=diag(length(lambda)),lambda) pmvSN(lower = rep(-Inf,length(lambda)),upper=rep(Inf,length(lambda)), mu = rep(0,length(lambda)),Sigma,lambda,log2 = FALSE) rmvSN(n,mu=rep(0,length(lambda)),Sigma=diag(length(lambda)),lambda)
x |
vector or matrix of quantiles. If |
n |
number of observations. |
lower |
the vector of lower limits of length p. |
upper |
the vector of upper limits of length p. |
mu |
a numeric vector of length p representing the location parameter. |
Sigma |
a numeric positive definite matrix with dimension pxp representing the scale parameter. |
lambda |
a numeric vector of length p representing the skewness parameter for SN and SN cases. If |
log2 |
a boolean variable, indicating if the log2 result should be returned. This is useful when the true probability is too small for the machine precision. |
dmvSN
gives the density, pmvSN
gives the distribution function, and rmvSN
generates random deviates for the Multivariate Skew-normal Distribution.
Christian E. Galarza <cgalarza88@gmail.com> and Victor H. Lachos <hlachos@uconn.edu>
Maintainer: Christian E. Galarza <cgalarza88@gmail.com>
Galarza, C. E., Matos, L. A., Dey, D. K., & Lachos, V. H. (2022a). "On moments of folded and doubly truncated multivariate extended skew-normal distributions." Journal of Computational and Graphical Statistics, 1-11 <doi:10.1080/10618600.2021.2000869>.
Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022b). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 <doi:10.1016/j.jmva.2021.104944>.
Galarza, C.E., Matos, L.A. and Lachos, V.H. (2022c). An EM algorithm for estimating the parameters of the multivariate skew-normal distribution with censored responses. Metron. <doi:10.1007/s40300-021-00227-4>.
Genz, A., (1992) "Numerical computation of multivariate normal probabilities," Journal of Computational and Graphical Statistics, 1, 141-149 <doi:10.1080/10618600.1992.10477010>.
dmvESN
, pmvESN
, rmvESN
, meanvarFMD
,meanvarTMD
,momentsTMD
#Univariate case dmvSN(x = -1,mu = 2,Sigma = 5,lambda = -2) rmvSN(n = 100,mu = 2,Sigma = 5,lambda = -2) #Multivariate case mu = c(0.1,0.2,0.3,0.4) Sigma = matrix(data = c(1,0.2,0.3,0.1,0.2,1,0.4,-0.1,0.3,0.4,1,0.2,0.1,-0.1,0.2,1), nrow = length(mu),ncol = length(mu),byrow = TRUE) lambda = c(-2,0,1,2) #One observation dmvSN(x = c(-2,-1,0,1),mu,Sigma,lambda) rmvSN(n = 100,mu,Sigma,lambda) #Many observations as matrix x = matrix(rnorm(4*10),ncol = 4,byrow = TRUE) dmvSN(x = x,mu,Sigma,lambda) lower = rep(-Inf,4) upper = c(-1,0,2,5) pmvSN(lower,upper,mu,Sigma,lambda)
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