FSDA

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Flexible Robust Statistics Data Analysis

NEWS: FSDA release 2024a is out. (April 2024)

GRAPHICS

Function fanplotFS.m now accepts in input not only the output of FSReda and FSRaddt but also the output of FSReda, Sregeda and MMregeda. fanplotFS can be used also to monitor the t-statistics coming from FS or S or MM regression.

New option corres inside resfwdplot. If corres is true a 3 panel plot showing the correlations of adjacent residuals is added to the monitoring residual plot.

New options colorBackground in spmplot in order to have a background color of each scatter which depends on the value of the correlation coefficient.

New option typespm which enables the user to control how the lower (upper) part of the scatter plot matrix is shown. It is possible to repalce the scatter with the correlation coefficients given as "circle", "square" "number". Finally it is also possible to suppress the scatter.

avasmsplot now returns the handle to the big (invisible) axes framing the subaxes of the plot.

New optional arguments in aceplot. Now it is possible to show just selected plots with or without the plot of the transformation for the explanatory variables.

New optional arguments addxline, flabstep and multiPanel in fanplotFS

New optional argument msg in FSRaddt

REGRESSION

Functions Sregeda, MMregeda, now also report the values of the t-statistics of the regression coefficients.

UTILITIES STAT

New functions pivotCoord and pivotCoordInv to transform the data into compute isometric logratio coordinates and viceversa.
New function logfactorial to compute log(x!) with high precision where x is not necessarily an integer.

DATASETS

New regression datasets, D1, D2, D3, inttrade1, inttrade2, inttrade3, cement, air_pollution, valueadded and nci60, added.

DISTRIBUTION

New functions WNChygepdf, WNChygecdf, WNChygeinv, WNChygernd, FNChygepdf, FNChygecdf, FNChygeinv, FNChygernd for the extended hypergeometric distribution.

All the functions which start with WNC refer to the Wallenius non central hypergeometric distribution, while all the functions which start with FNC refer to the Fisher non central hypergeometric distribution.

New functions mWNChygepdf, mWNChygernd, mFNChygepdf, mFNChygernd to compute the density and to generate random number from the multivariate Wallenius and multivariate Fisher non central hypergeometric distribution. All these functions are a translation from C++ of the routines of Fog (2008), library BiasedUrn.

VOLATILITY

A collection of routines

to compute the integrated variance from a diffusion process via the Fourier estimator using Dirichlet or Fejer kernel;
to computes the integrated variance, quarticity, leverage of a diffusion process via the Fourier-Malliavin estimator, routines written by S. Sanfelici and G. Toscano, (2024).

MULTIVARIATE

Functions for robust correspondence analysis completely redesigned mcdCorAna (mcd in correspondece analysis), FSCorAna (automatic outlier detection based on the Forward Search) and FSCorAnaenv (envelope creation of minimum Mahalanobis distance and inertia explained), FSCorAnaeda (FS in correspondence analysis with exploratory data analysis purposes).

APPS

CorAnaAPP which enables an interactive robust data analysis of the contingency tables

Oldest version of MATLAB which is supported is now R2019a.

Funcion fanplot.m in order to avoid conflicts with function fanplot of the Financial Toolbox has been renamed fanplotFS.m. Old function fanplot.m has been left in the folder but it will be removed in a future release.

Function playbackdemoFS.m has been removed.

Thanks to MathWorks support now the FSDA tree has a new structure which improves the toolbox’s polish, ease of maintenance, and developer/user experience. More specifically, subfolder +aux has been created in order to host the routines which do no have to be called directly by the user. Subfolders private have been added to each folder in order to host routines which are called just by the corresponding parent folder.

NEWS: new features (January 2024)

Now FSDA is compliant with MATLAB Toolbox Best Practices. Thanks to the constant support of Rob Purser and Bensingh Pancras we were able to migrate the FSDA toolbox to the new structure which improves the toolbox’s polish, ease of maintenance, and developer/user experience. In addition to being more GitHub-friendly, being in the standard format allows for a number of additional features that we are now attempting to leverage, such as namespaces and internal folders, among many other things. When a toolbox hits the 300 functions mark, it’s time to do some housekeeping!

NEWS: new features (June 2023)

Now FSDA supports the new buildtool feature (follow the link for more info) to create new releases on GitHub automatically leveraging the sinergy of the new buildtool functionalities, (see an overview in the documentation, available since R2022b) MATLAB scripts and GitHub Actions. Releasing a new FSDA release was a manual, multi-step process, that involved a lot of tasks in different environments, now the process runs entirely on GitHub and is consistent and fast. (Our thanks goes to Jos Martin, Rob Purser, Bensingh Pancras, Andy Campbell, Mark Cafaro et. al. that supported us with the implementation of this new feature)
Now FSDA is also availble as a Docker (follow the link for more info) so if you need to run simulations on a HPC facility on Singularity/Apptainer or you just want to try FSDA with all the features you can follow this link and download a full fledged FSDA docker (yes it works also locally on WSL/WSL2!). Once a new release is created, a docker of FSDA is automatically build and can be easily pulled. (Our thanks go to Jos Martin that helped us a lot on this project).

FSDA

This project hosts the source code to the original MATLAB FileExchange project and is place of active development.

FSDA Toolbox™ provides statisticians, engineers, scientists, researchers, financial analysts with a comprehensive set of tools to assess and understand their data. Flexible Statistics Data Analysis Toolbox™ software includes functions and interactive tools for analyzing and modeling data, learning and teaching statistics.

The Flexible Statistics Data Analysis Toolbox™ supports a set of routines to develop robust and efficient analysis of complex data sets (multivariate, regression, clustering, …), ensuring an output unaffected by anomalies or deviations from model assumptions.

In addition, it offers a rich set interactive graphical tools which enable us to explore the connection in the various features of the different forward plots.

All Flexible Statistics Data Analysis Toolbox™ functions are written in the open MATLAB® language. This means that you can inspect the algorithms, modify the source code, and create your own custom functions.

For the details about the functions present in FSDA you can browse the categorial and alphabetical list of functions of the toolbox inside MATLAB (once FSDA is installed) or at the web addresses http://rosa.unipr.it/FSDA/function-cate.html and http://rosa.unipr.it/FSDA/function-alpha.html

FSDA

Is especially useful in detecting in data potential anomalies (outliers), even when they occur in groups. Can be used to identify sub-groups in heterogeneous data.
Extends functionalities in key statistical domains requiring robust analysis (cluster analysis, discriminant analysis, model selection, data transformation).
Integrates instruments for interactive data visualization and modern exploratory data analysis, designed to simplify the interpretation of the statistical results by the end user.
Provides statisticians, engineers, scientists, financial analysts a comprehensive set of tools to assess and understand their data.
Provides practitioners, students and teachers with functions and graphical tools for modeling complex data, learning and teaching statistics.

FSDA is developed for wide applicability. For its capacity to address problems focusing on anomalies in the data, it is expected that it will be used in applications such as anti-fraud, detection of computer network intrusions, e-commerce and credit cards frauds, customer and market segmentation, detection of spurious signals in data acquisition systems, in chemometrics (a wide field covering biochemistry, medicine, biology and chemical engineering), in issues related to the production of official statistics (e.g. imputation and data quality checks), and so on.

For more information see the Wiki page at https://github.com/UniprJRC/FSDA/wiki

Ways to familiarize with the FSDA toolbox

Run the examples contained in files examples_regression.m or examples_multivariate.m or examples_categorical.m. Notice that all examples are organized in cells
Run the GUIs in the FSDA Matlab help pages. For a preview see http://rosa.unipr.it/FSDA/examples.html
Watch the videos in the Examples section of the FSDA Matlab help pages For a preview see http://rosa.unipr.it/fsda_video.html
Read section “Introduction to robust statistics” or “Technical introduction to Robust Statistics” in the FSDA Matlab help pages. For a preview see http://rosa.unipr.it/FSDA/tutorials.html

Installing the toolbox

The installation procedure is fully automatic if FSDA is installed through Get Adds-Ons inside MATLAB.

The most critical part of the installation concerns the FSDA documentation system, which consists in a series of HTML files that follow the typical MATLAB style and are completely integrated inside the MATLAB documentation system.

The html help files can be found in the Supplemental Software tab** which appears at the bottom of the Doc Center home page (see screenshot below).

drawing

Similarl to what happens for the MATLAB documentation, the FSDA documentation is shown in a different way depending on the User Preferences.

Inside Preferences the Documentation Location is “Web”.

In this case every time the user the user tries to access FSDA documentation it will be redirected to the appropriate page inside http://rosa.unipr.it/FSDA (of course in this case Internet connection is required).

Inside Preferences the Documentation Location is “Installed Locally”.

In this case the user has installed the documentation locally. The first time the user tries to access the FSDA documentation, there is a menu “Copy FSDA HTML help files” which alertes the user that the FSDA help files need to be copied in the local documentation folder.

drawing

If the user clicks on Yes the files will be copied under the subfolder help of the documentation root folder. If the user clicks on No the user will be redirected to the on line documentation of FSDA.

Remark: in order to understand the path of your documentation root folder in your machine it is enough to type docroot in the prompt.

If everything went well independently of your Documentation Location prefereces you should be able to see the The FSDA entry page, as shown below:

Remark: you can reach our main documentation page also simply typing docsearchFS in the command prompt

From our main documentation page you can go to the Examples page (see screenshot below),

where you can find GUIs, example codes (see screenshot below),

and links to videos containing the analysis of selected examples (see screenshot below).

From any point of our documentation system you can go to the “Tutorials” page (see screenshot below)

where you can find several tutorials about robust statistics and dynamic statistical visualization, transformations…. (see screenshot below).

On the other hand, if from the left menu one clicks on “Functions and Other References” (see screenshot above), it is possible to get the categorical list of functions present in the toolbox (see screenshot below).

Of course clicking on the button it is possible to browse in alphabetical order the documentation of the 210 functions present inside the FSDA toolbox (see screenshot below).

By clicking on one of these links (for example on tclust, see screenshot above) it is possible to reach the HTML documentation of the function in a perfect new MATLAB documentation style (see screenshot below).

These HTML documentation pages have been created automatically by our routines publishFS. Every HTML documentation contains a series of Examples and Related Examples.

The icon at the beginning of the line, indicates that the associated example has been executed and its output has been captured inside the HTML file. For example, if you click on the first of the Related Examples (see screenshot below),

it is possible to see both the code (note that the code is displayed inside HTML using typical Matlab colouring) and the output which was generated (see the two screenshots below).

In the More About section of our HTML files (see screenshot below), it is possible find the theoretical background which accompanies a particular function.

For example, the screenshot below shows what you get in the case of function tclust.

Remark: there is a one to one correspondence between the documentation contained inside the .m file and the corresponding .html file.

The documentation inside the .m file can be easily accessed from the command prompt typing help and the name of the function.

For example, the screenshot below shows what you get if you type in the prompt “help MixSim”.

Sometimes inside the .m file (especially in the section “More About”) we have added a number of formulae in latex language (see screenshot below).

Clearly all these latex formulae will show up correctly (thanks to MathJax technology) in the corresponding HTML help page. For example, in the case of MixSim function, in the command window, by clicking on the link “Link to the help function”,

one is redirected to the corresponding HTML documentation page. Here, in the “More About” section it is possible to see the code in proper mathematical style.

Finally, it is worthwhile to remark that it is possible to go directly to the HTML documentation page simply typing docsearchFS and the name of the requested function. For example, in the case of tclust to reach file tclust.html it is possible to type:

Generally, the output of our functions is a structure, which contains several fields, documented in detail inside the initial part of the .m function. For example, in the case of tclust inside tclust.m it is possible to navigate to section Output(see screenshot below):

In the corresponding HTML file our parser publishFS.m puts all the fields of input and output structure inside a HTML table (see screenshot below):

Every subfolder of FSDA contains file contents.m (automatically created by our routine makecontentsfileFS.m) which contains a series of detailed information about all the .m files of the folder, which have the corresponding HTML documentation. For example, the screenshot referred to the left part of file contents.m inside subfolder “utilities” is given below.

Similarly, inside the main root of FSDA file contents.m lists in alphabetical order all files present in all subfolders of FSDA, which have the corresponding HTML page (see screenshot below):

Installation notes (details)

If FSDA has been installed properly (in what follows without loss of generality we assume, for example, that FSDA has been installed in folder D:\matlab\FSDA), after the installation the “Set Path” window of MATLAB should include the following FSDA search paths

when FSDA is installed three APPS (brushRES, brushFAN and brushROB) are automatically installed:

Remark: if the three APPS have not been automatically installed, you can easily install them manually by double clicking on the files brushFAN.mlappinstall, brushRES.mlappinstall and brushROB.mlappinstall contained in the subfolder (FSDAfolder)/toolbox/apps.

These APPS are graphical user interfaces conceived to demonstrate some functionalities of FSDA.

Three example files named “examples_regression.m”, “examples_multivariate.m”, and “examples_categorical.m” can be found in (FSDAfolder)/toolbox/examples These files contain a series analysis of several well-known datasets in the literature of robust statistics and categorical data analysis and have the purpose to let the user familiarize with the toolbox

If you think that something not described in these notes went wrong please do not hesitate to send an e-mail to

FSDA@unipr.it

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