XLSTAT-ADA

by Addinsoft

XLSTAT-ADA (ADA stands for Advanced Data Analysis) makes it possible for XLSTAT users to run multiple tables methods. These methods are useful for a variety of applications, ranging from ecology to marketing. XLSTAT-ADA contains currently the following features:

CANONICAL CORRESPONDENCE ANALYSIS (CCA AND PARTIAL CCA)
Use Canonical Correspondence Analysis (CCA) to analyze a contingency table (typically with sites as rows and species in columns) while taking into account the information provided by a set of explanatory variables contained in a second table and measured on the same sites.

Canonical Correspondence Analysis (CCA) has been developed to allow ecologists to relate the abundance of species to environmental variables (Ter Braak, 1986). However, this method can be used in other domains. Geomarketing and demographic analyses should be able to take advantage of it.

XLSTAT allows to remove the information provided in third table, using Partial CCA. A permutation test is also available to test if the relationship between the contingency table and the explanatory variables is significant or not.

GENERALIZED PROCRUSTEAN ANALYSIS (GPA)
GPA is used in sensory data analysis before a Preference Mapping to reduce the scale effects and to obtain a consensual configuration. It also allows to compare the proximity between the terms that are used by different experts to describe products.

MULTIPLE FACTOR ANALYSIS (MFA)
Use the Multiple Factor Analysis (MFA) to simultaneously analyze several tables of variables, and to obtain results, particularly charts, that allow to study the relationship between the observations, the variables and the tables. Within a table, the variables must be of the same type (quantitative or qualitative), but the tables can be of different types.

REDUNDANCY ANALYSIS (RDA)
Redundancy Analysis (RDA) has been developed by Van den Wollenberg (1977) as an alternative to Canonical Correlation Analysis (CCorA). RDA allows studying the relationship between two tables of variables Y and X. While the CCorA is a symmetric method, RDA is non-symmetric. In CCorA, the components extracted from both tables are such that their correlation is maximized. In RDA, the components extracted from X are such that they are as much as possible correlated with the variables of Y. Then, the components of Y are extracted so that they are as much as possible correlated with the components extracted from X.

PRINCIPAL COORDINATE ANALYSIS
Use Principal Coordinate Analysis to graphically visualize a square matrix that describes the similarity or the dissimilarity between p elements (individuals, variables, objects, …).