fsdaR

This package provides interface to the ‘MATLAB’ toolbox ‘Flexible Statistical Data Analysis (FSDA)’ which is comprehensive and computationally efficient software package for robust statistics in regression, multivariate and categorical data analysis. The current R version implements tools for regression: (forward search, S- and MM-estimation, least trimmed squares (LTS) and least median of squares (LMS)), for multivariate analysis (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression. The distinctive feature of our package is the possibility of monitoring the statistics of interest as a function of breakdown point, efficiency or subset size, depending on the estimator. This is accompanied by a rich set of graphical features, such as dynamic brushing, linking, particularly useful for exploratory data analysis.

Installation

The fsdaR package is on CRAN (The Comprehensive R Archive Network) and the latest release can be easily installed using the command

install.packages("fsdaR")

NOTE (WINDOWS): To avoid some spurious errors due to the new MCR installation on Windows (V9.10), add the following to the system path

<RUNTIME_ROOT>\bin\win64

which most probably boils down to:

C:\Program Files\MATLAB\MATLAB Runtime\v910\bin\win64

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("UniprJRC/fsdaR")

Of course, if you have already installed remotes, you can skip the first line (I have commented it out).

On Windows, follow the NOTE given above.

Example

This is a basic example which shows you if the package is properly installed:

library(fsdaR)
#> Robust Data Analysis Through Monitoring and Dynamic
#> Visualization (version 0.8-1)

n <- 200
v <- 3
set.seed(123456)
X <- matrix(rnorm(n*v), nrow=n)

Xcont <- X
Xcont[1:5, ] <- Xcont[1:5,] + 3

out <- fsmult(Xcont, trace=TRUE)               # no plots (plot defaults to FALSE)
#> 
#> Optional parameters to FSM(): 
#> $plots
#> [1] 0
#> 
#> $crit
#> [1] "md"
#> 
#> $rf
#> [1] 0.95
#> 
#> $msg
#> [1] 1
#> 
#> $nocheck
#> [1] 0
#> 
#> This is the very first call to the FSDA engine, 
#> it can take some time to initialize it ...
#> 
#> 
#> Returning from FSM().  Fields returned by MATLAB: 
#> [1] "outliers" "loc"      "cov"      "md"       "mmd"      "Un"       "nout"    
#> [8] "class"
names(out)
#> [1] "call"     "outliers" "loc"      "cov"      "md"       "X"        "mmd"     
#> [8] "Un"       "nout"

out <- fsmult(Xcont, trace=TRUE, plot=TRUE)    # identical to plots=1
#> 
#> Optional parameters to FSM(): 
#> $plots
#> [1] 1
#> 
#> $crit
#> [1] "md"
#> 
#> $rf
#> [1] 0.95
#> 
#> $msg
#> [1] 1
#> 
#> $nocheck
#> [1] 0
#> 
#> 
#> Returning from FSM().  Fields returned by MATLAB: 
#> [1] "outliers" "loc"      "cov"      "md"       "mmd"      "Un"       "nout"    
#> [8] "class"