NPflow (version 0.9.0)

mmvsnpdfC: C++ implementation of multivariate skew Normal probability density function for multiple inputs

Description

C++ implementation of multivariate skew Normal probability density function for multiple inputs

Usage

mmvsnpdfC(x, xi, psi, sigma, Log = TRUE)

Arguments

x

data matrix of dimension p x n, p being the dimension of the data and n the number of data points.

xi

mean vectors matrix of dimension p x K, K being the number of distributions for which the density probability has to be evaluated.

psi

skew parameter vectors matrix of dimension p x K.

sigma

list of length K of variance-covariance matrices, each of dimensions p x p.

Log

logical flag for returning the log of the probability density function. Defaults is TRUE.

Value

• matrix of densities of dimension K x n.

Examples

mmvsnpdfC(x=matrix(rep(1.96,2), nrow=2, ncol=1),
         xi=matrix(c(0, 0)), psi=matrix(c(1, 1),ncol=1), sigma=list(diag(2)), Log=FALSE
         )
mmvsnpdfC(x=matrix(rep(1.96,2), nrow=2, ncol=1),
         xi=matrix(c(0, 0)), psi=matrix(c(1, 1),ncol=1), sigma=list(diag(2))
         )

library(microbenchmark)
microbenchmark(mvsnpdf(x=matrix(rep(1.96,2), nrow=2, ncol=1), xi=c(0, 0), psi=c(1, 1),
                      sigma=diag(2), Log=FALSE),
              mmvsnpdfC(x=matrix(rep(1.96,2), nrow=2, ncol=1), xi=matrix(c(0, 0)),
                        psi=matrix(c(1, 1),ncol=1), sigma=list(diag(2)), Log=FALSE),
              times=1000L
             )
microbenchmark(mvsnpdf(x=matrix(c(rep(1.96,2),rep(0,2)), nrow=2, ncol=2),
                     xi=list(c(0,0),c(-1,-1), c(1.5,1.5)),
                     psi=list(c(0.1,0.1),c(-0.1,-1), c(0.5,-1.5)),
                     sigma=list(diag(2),10*diag(2), 20*diag(2)), Log=FALSE),
              mmvsnpdfC(matrix(c(rep(1.96,2),rep(0,2)), nrow=2, ncol=2),
                     xi=matrix(c(0,0,-1,-1, 1.5,1.5), nrow=2, ncol=3),
                     psi=matrix(c(0.1,0.1,-0.1,-1, 0.5,-1.5), nrow=2, ncol=3),
                     sigma=list(diag(2),10*diag(2), 20*diag(2)), Log=FALSE),
              times=1000L)