Session 1: Software
http://hdl.handle.net/10256/639
2024-03-28T21:13:16Z
2024-03-28T21:13:16Z
News from compositions, the R package
Bren, Matevž
Tolosana Delgado, Raimon
Boogaart, K. Gerald van den
http://hdl.handle.net/10256/716
2022-07-13T06:59:26Z
2008-05-27T00:00:00Z
News from compositions, the R package
Bren, Matevž; Tolosana Delgado, Raimon; Boogaart, K. Gerald van den
Daunis-i-Estadella, Pepus; Martín Fernández, Josep Antoni
The R-package “compositions”is a tool for advanced compositional analysis. Its basic
functionality has seen some conceptual improvement, containing now some facilities
to work with and represent ilr bases built from balances, and an elaborated subsys-
tem for dealing with several kinds of irregular data: (rounded or structural) zeroes,
incomplete observations and outliers. The general approach to these irregularities is
based on subcompositions: for an irregular datum, one can distinguish a “regular” sub-
composition (where all parts are actually observed and the datum behaves typically)
and a “problematic” subcomposition (with those unobserved, zero or rounded parts, or
else where the datum shows an erratic or atypical behaviour). Systematic classification
schemes are proposed for both outliers and missing values (including zeros) focusing on
the nature of irregularities in the datum subcomposition(s).
To compute statistics with values missing at random and structural zeros, a projection
approach is implemented: a given datum contributes to the estimation of the desired
parameters only on the subcompositon where it was observed. For data sets with
values below the detection limit, two different approaches are provided: the well-known
imputation technique, and also the projection approach.
To compute statistics in the presence of outliers, robust statistics are adapted to the
characteristics of compositional data, based on the minimum covariance determinant
approach. The outlier classification is based on four different models of outlier occur-
rence and Monte-Carlo-based tests for their characterization. Furthermore the package
provides special plots helping to understand the nature of outliers in the dataset.
Keywords: coda-dendrogram, lost values, MAR, missing data, MCD estimator,
robustness, rounded zeros
2008-05-27T00:00:00Z
CODAPACK3D. A new version of Compositional Data Package
Thió i Fernández de Henestrosa, Santiago
Gómez, O.
Cepero i Sánchez, Ricard
http://hdl.handle.net/10256/715
2022-07-13T06:59:26Z
2008-05-27T00:00:00Z
CODAPACK3D. A new version of Compositional Data Package
Thió i Fernández de Henestrosa, Santiago; Gómez, O.; Cepero i Sánchez, Ricard
Daunis-i-Estadella, Pepus; Martín Fernández, Josep Antoni
The statistical analysis of compositional data should be treated using logratios of parts,
which are difficult to use correctly in standard statistical packages. For this reason a
freeware package, named CoDaPack was created. This software implements most of the
basic statistical methods suitable for compositional data.
In this paper we describe the new version of the package that now is called
CoDaPack3D. It is developed in Visual Basic for applications (associated with Excel©),
Visual Basic and Open GL, and it is oriented towards users with a minimum knowledge
of computers with the aim at being simple and easy to use.
This new version includes new graphical output in 2D and 3D. These outputs could be
zoomed and, in 3D, rotated. Also a customization menu is included and outputs could
be saved in jpeg format. Also this new version includes an interactive help and all
dialog windows have been improved in order to facilitate its use.
To use CoDaPack one has to access Excel© and introduce the data in a standard
spreadsheet. These should be organized as a matrix where Excel© rows correspond to
the observations and columns to the parts. The user executes macros that return
numerical or graphical results. There are two kinds of numerical results: new variables
and descriptive statistics, and both appear on the same sheet. Graphical output appears
in independent windows. In the present version there are 8 menus, with a total of 38
submenus which, after some dialogue, directly call the corresponding macro. The
dialogues ask the user to input variables and further parameters needed, as well as
where to put these results. The web site http://ima.udg.es/CoDaPack contains this
freeware package and only Microsoft Excel© under Microsoft Windows© is required to
run the software.
Kew words: Compositional data Analysis, Software
2008-05-27T00:00:00Z