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README.Rmd
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---
output: github_document
---
DepthProc
========================================================
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, echo=FALSE}
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.
### Versions
#### CRAN release version
[](https://github.com/zzawadz/DepthProc/stargazers)
[](https://github.com/zzawadz/DepthProc)
[](https://CRAN.R-project.org/package=DepthProc)
[](https://CRAN.R-project.org/package=DepthProc)
[](https://CRAN.R-project.org/package=DepthProc)
[](https://travis-ci.org/zzawadz/DepthProc)
[](https://ci.appveyor.com/project/zzawadz/DepthProc)
[](https://codecov.io/github/zzawadz/DepthProc?branch=master)
[](https://www.repostatus.org/#active)
## Installation
*DepthProc* is avaiable on CRAN:
```{r, eval=FALSE}
install.packages("DepthProc")
```
You can also install it from GitHub with *devtools* package:
```{r, eval=FALSE}
library(devtools)
install_github("zzawadz/DepthProc")
```
## Main features:
### Speed and multithreading
Most of the code is written in C++ for additional efficiency. We also use OpenMP to speedup computations with multithreading:
```{r, message=FALSE, warning=FALSE, echo=TRUE, results='markup'}
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))
```
## Available depth functions
```{r}
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")))
```
## Basic plots
### Contour plot
```{r contour}
library(mvtnorm)
y <- rmvt(n = 200, sigma = diag(2), df = 4, delta = c(3, 5))
depthContour(y, points = TRUE, graph_params = list(lwd = 2))
```
### Perspective plot
```{r persp}
depthPersp(y, depth_params = list(method = "Mahalanobis"))
```
## Functional depths:
There are two functional depths implemented - modified band depth (MBD), and Frainman-Muniz depth (FM):
```{r}
x <- matrix(rnorm(60), nc = 20)
fncDepth(x, method = "MBD")
fncDepth(x, method = "FM", dep1d = "Mahalanobis")
```
### Functional BoxPlot
```{r fncBox}
x <- matrix(rnorm(2000), ncol = 100)
fncBoxPlot(x, bands = c(0, 0.5, 1), method = "FM")
```