set.seed(123) knitr::opts_chunk$set( echo = TRUE, results="hide", collapse = TRUE, comment = "#>", fig.path = "man/figures/README-" )
DepthProc project consist of a set of statistical procedures based on so called statistical depth functions. The project involves free available R package and its description.
DepthProc is avaiable on CRAN:
install.packages("DepthProc")
You can also install it from GitHub with devtools package:
library(devtools) install_github("zzawadz/DepthProc")
Most of the code is written in C++ for additional efficiency. We also use OpenMP to speedup computations with multithreading:
library(DepthProc) set.seed(123) d <- 10 x <- mvrnorm(1000, rep(0, d), diag(d)) # Default - utilize as many threads as possible system.time(depth(x, x, method = "LP")) # Only single thread - 4 times slower: system.time(depth(x, x, method = "LP", threads = 1)) # Two threads - 2 times slower: system.time(depth(x, x, method = "LP", threads = 2))
x <- mvrnorm(100, c(0, 0), diag(2)) depthEuclid(x, x) depthMah(x, x) depthLP(x, x) depthProjection(x, x) depthLocal(x, x) depthTukey(x, x) ## Base function to call others: depth(x, x, method = "Projection") depth(x, x, method = "Local", depth_params1 = list(method = "LP")) ## Get median depthMedian(x, depth_params = list( method = "Local", depth_params1 = list(method = "LP")))
library(mvtnorm) y <- rmvt(n = 200, sigma = diag(2), df = 4, delta = c(3, 5)) depthContour(y, points = TRUE, graph_params = list(lwd = 2))
depthPersp(y, depth_params = list(method = "Mahalanobis"))
There are two functional depths implemented - modified band depth (MBD), and Frainman-Muniz depth (FM):
x <- matrix(rnorm(60), nc = 20) fncDepth(x, method = "MBD") fncDepth(x, method = "FM", dep1d = "Mahalanobis")
x <- matrix(rnorm(2000), ncol = 100) fncBoxPlot(x, bands = c(0, 0.5, 1), method = "FM")
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