Description Usage Arguments Value Examples
C++ implementation of multivariate Normal probability density function for multiple inputs
1 |
x |
data matrix of dimension |
mean |
mean vectors matrix of dimension |
varcovM |
list of length |
Log |
logical flag for returning the log of the probability density
function. Defaults is |
matrix of densities of dimension K x n
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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: Rcpp
Loading required package: truncnorm
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 cld
277.666 303.8775 336.00307 324.1285 344.371 3797.873 1000 b
15.441 19.3730 26.10893 27.0270 30.174 101.104 1000 a
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