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

Usage

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

if(require(microbenchmark)){
library(microbenchmark)
microbenchmark(mvnpdf(x=matrix(1.96), mean=0, varcovM=diag(1), Log=FALSE),
               mvnpdfC(x=matrix(1.96), mean=0, varcovM=diag(1), Log=FALSE),
               mmvnpdfC(x=matrix(1.96), mean=matrix(0), varcovM=list(diag(1)), Log=FALSE),
               times=1000L)
microbenchmark(mvnpdf(x=matrix(rep(1.96,2), nrow=2, ncol=1), mean=c(-0.2, 0.3),
                      varcovM=matrix(c(2, 0.2, 0.2, 2), ncol=2), Log=FALSE),
               mvnpdfC(x=matrix(rep(1.96,2), nrow=2, ncol=1), mean=c(-0.2, 0.3),
                       varcovM=matrix(c(2, 0.2, 0.2, 2), ncol=2), Log=FALSE),
               mmvnpdfC(x=matrix(rep(1.96,2), nrow=2, ncol=1),
                        mean=matrix(c(-0.2, 0.3), nrow=2, ncol=1),
                        varcovM=list(matrix(c(2, 0.2, 0.2, 2), ncol=2)), Log=FALSE),
               times=1000L)
microbenchmark(mvnpdf(x=matrix(c(rep(1.96,2),rep(0,2)), nrow=2, ncol=2),
                      mean=list(c(0,0),c(-1,-1), c(1.5,1.5)),
                      varcovM=list(diag(2),10*diag(2), 20*diag(2)), Log=FALSE),
               mmvnpdfC(matrix(c(rep(1.96,2),rep(0,2)), nrow=2, ncol=2),
                        mean=matrix(c(0,0,-1,-1, 1.5,1.5), nrow=2, ncol=3),
                        varcovM=list(diag(2),10*diag(2), 20*diag(2)), Log=FALSE),
               times=1000L)
}else{
cat("package 'microbenchmark' not available\n")
}
#> Loading required package: microbenchmark
#> Unit: microseconds
#>                                                                                                                                                                                                        expr
#>            mvnpdf(x = matrix(c(rep(1.96, 2), rep(0, 2)), nrow = 2, ncol = 2),      mean = list(c(0, 0), c(-1, -1), c(1.5, 1.5)), varcovM = list(diag(2),          10 * diag(2), 20 * diag(2)), Log = FALSE)
#>  mmvnpdfC(matrix(c(rep(1.96, 2), rep(0, 2)), nrow = 2, ncol = 2),      mean = matrix(c(0, 0, -1, -1, 1.5, 1.5), nrow = 2, ncol = 3),      varcovM = list(diag(2), 10 * diag(2), 20 * diag(2)), Log = FALSE)
#>      min       lq      mean   median       uq      max neval
#>  219.679 225.8115 246.06029 230.7745 242.2965 3087.516  1000
#>   11.291  12.5735  15.56035  16.5060  17.5120   55.534  1000