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

Usage

mmvtpdfC(x, mean, varcovM, df, 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.
df: vector of length K of degree of freedom parameters.
Log: logical flag for returning the log of the probability density function. Defaults is TRUE.

Value

matrix of densities of dimension K x n.

Author

Boris Hejblum

Examples

mvnpdf(x=matrix(1.96), mean=0, varcovM=diag(1), Log=FALSE)
#> [1] 0.05844094
mvtpdf(x=matrix(1.96), mean=0, varcovM=diag(1), df=10000000, Log=FALSE)
#> [1] 0.05844095
mmvtpdfC(x=matrix(1.96), mean=matrix(0), varcovM=list(diag(1)), df=10000000, Log=FALSE)
#>            [,1]
#> [1,] 0.05844095

mvnpdf(x=matrix(1.96), mean=0, varcovM=diag(1))
#> [1] -2.839739
mvtpdf(x=matrix(1.96), mean=0, varcovM=diag(1), df=10000000)
#> [1] -2.839738
mmvtpdfC(x=matrix(1.96), mean=matrix(0), varcovM=list(diag(1)), df=10000000)
#>           [,1]
#> [1,] -2.839738

mvtpdf(x=matrix(1.96), mean=0, varcovM=diag(1), df=10)
#> [1] -2.731911
mmvtpdfC(x=matrix(1.96), mean=matrix(0), varcovM=list(diag(1)), df=10)
#>           [,1]
#> [1,] -2.731911


if(require(microbenchmark)){
library(microbenchmark)
microbenchmark(mvtpdf(x=matrix(1.96), mean=0, varcovM=diag(1), df=1, Log=FALSE),
               mmvtpdfC(x=matrix(1.96), mean=matrix(0), varcovM=list(diag(1)),
                        df=c(1), Log=FALSE),
               times=10000L)
}else{
cat("package 'microbenchmark' not available\n")
}
#> Unit: microseconds
#>                                                                                                expr
#>                     mvtpdf(x = matrix(1.96), mean = 0, varcovM = diag(1), df = 1,      Log = FALSE)
#>  mmvtpdfC(x = matrix(1.96), mean = matrix(0), varcovM = list(diag(1)),      df = c(1), Log = FALSE)
#>     min     lq      mean median     uq      max neval
#>  53.350 56.054 61.775994 57.166 58.819 2916.766 10000
#>   6.232  6.903  8.784179  8.396  8.966 2795.321 10000