Marco Riani, Professor of Statistics

      Univ. of Parma (ITALY)

MATLAB ROUTINES

 

FSDA Toolbox extends MATLAB and statistics toolbox to support a robust and efficient analysis of complex data sets.

FSDA was first released on March 2012.

Last update: May  22, 2017

LINK to the main documentation page of FSDA

 

You can have a look at the toolbox features through a short video or through some didactic movies

Movie 1: Hawkins data

Movie 2: Fishery data

Movie 3: AR data

Movie 4: Loyalty cards data

Movie 5: Hospital data

 

Installation Notes

In the Installation Notes (MAY 2017) we tried to document all that you have to expect when FSDA is installed manually by unpacking the compressed tar file FSDA.tar.gz, or automatically with our setup program for Windows platforms.

Download FSDA A setup executable for MS Windows platforms will install the toolbox and update the search path of your local MATLAB installation.

Download a compressed tar file of the toolbox, suitable for Unix platforms installation. In this case, you have to add manually FSDA folder and sub-folders to the MATLAB path or use our routine addFSDA2path.

Download a working paper describing the main characteristics of the FSDA toolbox or see Riani, Perrotta, Torti (2012).

 

Highlights of the last releases

May 2017 Release 2017A

FSDA follows typical MATLAB timetable in the sense that there are two releases per year. The first typically in May/June and the second around October/November.

We decided to label FSDA release with the most recent MATLAB version to which it is associated.

Major statistical release. Highlights:
  • CLUSTERING
    Function tclustreg now includes trimmed Cluster Weighted Restricted Models.
    New function tclustIC for the automatic selection of the best number of groups.
    New function tlclustICsol to extract a set of relevant solutions (and associated tclustICplot).
    New function UnitsSameCluster to to control the labels of the clusters which contain predefined units.
    New function to compare two partitions (Fowlkes and Mallows index)
    Updated routine simdatasetreg to generate new outlier patterns

  • STATISTICAL UTILITIES

    New routines for density estimation and thinning, for univariate and bivariate data (used in tclustreg).
    bwe, rthin, wthin, WNChygepdf.

  • UTILITIES
    New functions wraptextFS, removeextraspacesLF

  • MONITORING ROBUST ESTIMATORS
    New functions mveeda, MMmulteda
    Smulteda, Sregeda

  • TRANSFORMATIONS IN REGRESSION
     New function ScoreYJ which implements the score test for the Yeo and Johnson transformation. This new transformation has also been embedded inside function FSRfan.

  • SAMPLING AND COMBINATORIAL
    updated functions randsampleFS and subsets.
    New routines for thinning.

  • NEW mlx files
    .mlx files introduced for examples_MixSim

  • GRAPHICS
    New function to create the car-bike plot to find the most relevant solutions (carbikeplot).
    Functions resfwdplot, malfwdplot generalized in order to take as input the output of procedures which monitor robust estimators.


  • The FSDA help folder now contains XML files associated to the functions documentation. This is in view of generating/updating automatically or using a GUI the functions documentation, in html as well as in the function head

 

 

 

October 2016 Release 2016b

Major statistical release. Highlights:
  • New functions for  bivariate density estimation and random thinning (kdebiv.m, rthin.m) used to extend tclustreg.m features.
  • The FSDA help folder now contains XML files associated to the functions documentation. This is in view of generating/updating automatically or using a GUI the functions documentation, in html as well as in the function head

 

 

 

June 2016 Release 2016a

Major statistical release. Highlights:
  • New features added to the tclust function, including determinant restriction and new adjusted BIC criterion for the estimation of the number of groups.
  • Added functions for reweighting FSR and FSRB (FSRr and FSRBr).
  • Functions FSR, FSRB and FSRH redesigned; a routing implementing the core of the Forward Search algorithm (FSRcore) introduced to avoid code redundancies.
  • New function, winsor, to winsor data.
  • New function FSMbsb, which will replace FSMbbm.
  • New function randindexFS, to evaluate the quality of different clusterings.
  • New routines poolClose and poolPrepare introduced to conveniently open and close a pool of parallel workers.
  • Several new robust functions to generate, for example, the Tukey Biweigh rho function (HUrho), the tuning constant associated to a certain efficiency (HUeff), the psi functions (HUpsi), its derivative (HUpsider), etc. For a full list, see functions under utilities_stats folder.

Major highlights in the documentations and examples:

  • New function, publishFS, introduced to generate documentation pages directly from the .m files.
  • New function, makecontentsfileFS, introduced to create a the list of files present in a FSDA folder and/or subfolders. It extends MATLAB function makecontentsfile.
  • .mlx files introduced for examples_multivariate and examples_regression

September 2015 Release 2015b

  • Function simdataset.m modified to allow the user to simulate outliers from different distributions and contamination schemes and/or contaminate existing datasets.
  • New Bayesian regression analysis routines: FSRB.m, FSRBeda.m, FSRBmdr.m, regressB.m.
  • In FSReda.m: monitoring of confidence intervals of beta and sigma2.
  • In FSRBeda.m: monitoring of HPD (highest posterior density regions) of beta and sigma2.
  • New functions for inverse gamma computation: inversegampdf.m, inversegamcdf.m, inversegaminv.m.
  • Added functions to monitor units forming subset in heterosckedastic and Bayesian regression: FSRHbsb.m, FSRBbsb.m.
  • Added new datasets for Bayesian examples.
  • Added option for the robust transformation in the Yeo-Johnson family.
  • addFSDA2path.m: modified for compatibility with unix platforms and to address changes in the folder organization of FSDA functions.
  • Added routines to compute and visualize robust bivariate boxplot (function boxplotb.m).
  • New routine for automatic outlier detection in heteroskedastic regression (FSRH.m).

Please, do not hesitate to contact us for any bug you might find and for any suggestion you might have!!! Thanks to all those who have already contacted us and have helped us to correct several bugs and improve the performance of the code.

fsda@unipr.it

Marco Riani, Andrea Cerioli and Aldo Corbellini (University of Parma)

Domenico Perrotta and Francesca Torti (EC, Joint Research Centre)

Patrizia Calcaterra, Andrea Cerasa, Emmanuele Sordini, Daniele Palermo (EC, Joint Research Centre)