Robust Multivariate Methods for High Dimensional Data.
rrcovHD: Robust Multivariate Methods for High Dimensional Data
The package rrcovHD provides robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) doi:10.1016/j.ins.2012.10.017), robust sparse PCA (Croux et al. (2013) doi:10.1080/00401706.2012.727746, Todorov and Filzmoser (2013) doi:10.1007/978-3-642-33042-1_31), robust PLS (Todorov and Filzmoser (2014) doi:10.17713/ajs.v43i4.44), and robust sparse classification (Ortner et al. (2020) doi:10.1007/s10618-019-00666-8).
Installation
The rrcovHD package is on CRAN (The Comprehensive R Archive Network) and the latest release can be easily installed using the command
install.packages("rrcovHD")
library(rrcovNA)
Building from source
To install the latest stable development version from GitHub, you can pull this repository and install it using
## install.packages("remotes")
remotes::install_github("valentint/rrcovHD")
Of course, if you have already installed remotes, you can skip the first line (I have commented it out).
Example
This is a basic example which shows you if the package is properly installed:
library(rrcovHD)
#> Loading required package: rrcov
#> Loading required package: robustbase
#> Scalable Robust Estimators with High Breakdown Point (version 1.7-5)
#> Robust Multivariate Methods for High Dimensional Data (version 0.2-7)
data(pottery)
dim(pottery) # 27 observations in 2 classes, 6 variables
#> [1] 27 7
head(pottery)
#> SI AL FE MG CA TI origin
#> 1 55.8 14.0 10.2 4.9 5.0 0.88 Attic
#> 2 51.2 12.5 10.1 4.4 4.8 0.86 Attic
#> 3 57.1 14.0 8.3 6.4 11.2 0.75 Attic
#> 4 53.8 13.1 9.3 4.9 6.6 0.81 Attic
#> 5 59.4 14.8 9.8 5.5 5.4 0.89 Attic
#> 6 56.2 14.0 9.9 4.9 5.4 0.89 Attic
## Build the SIMCA model. Use RSimca for a robust version
rs <- RSimca(origin~., data=pottery)
rs
#> Call:
#> RSimca(origin ~ ., data = pottery)
#>
#> Prior Probabilities of Groups:
#> Attic Eritrean
#> 0.4814815 0.5185185
#>
#> Pca objects for Groups:
#>
#> Call:
#> PcaHubert(x = class, k = k[i], kmax = kmax[i], trace = trace)
#> Importance of components:
#> PC1 PC2
#> Standard deviation 5.2672 0.8564
#> Proportion of Variance 0.7186 0.1804
#> Cumulative Proportion 0.7186 0.8990
#>
#> Call:
#> PcaHubert(x = class, k = k[i], kmax = kmax[i], trace = trace)
#> Importance of components:
#> PC1
#> Standard deviation 3.2934
#> Proportion of Variance 0.8102
#> Cumulative Proportion 0.8102
summary(rs)
#>
#> Call:
#> RSimca(formula = origin ~ ., data = pottery)
#>
#> Prior Probabilities of Groups:
#> Attic Eritrean
#> 0.4814815 0.5185185
#>
#> Pca objects for Groups:
#>
#> Call:
#> PcaHubert(x = class, k = k[i], kmax = kmax[i], trace = trace)
#> Importance of components:
#> PC1 PC2
#> Standard deviation 5.2672 0.8564
#> Proportion of Variance 0.7186 0.1804
#> Cumulative Proportion 0.7186 0.8990
#>
#> Call:
#> PcaHubert(x = class, k = k[i], kmax = kmax[i], trace = trace)
#> Importance of components:
#> PC1
#> Standard deviation 3.2934
#> Proportion of Variance 0.8102
#> Cumulative Proportion 0.8102
Community guidelines
Report issues and request features
If you experience any bugs or issues or if you have any suggestions for additional features, please submit an issue via the Issues tab of this repository. Please have a look at existing issues first to see if your problem or feature request has already been discussed.
Contribute to the package
If you want to contribute to the package, you can fork this repository and create a pull request after implementing the desired functionality.
Ask for help
If you need help using the package, or if you are interested in collaborations related to this project, please get in touch with the package maintainer.