MultipleCorrespondence Class |
Namespace: Novacta.Analytics
The MultipleCorrespondence type exposes the following members.
Name | Description | |
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Categories |
Gets the principal projections of the cloud of categories.
| |
Individuals |
Gets the principal projections of the cloud of individuals.
|
Name | Description | |
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Analyze |
Analyzes the multiple correspondence of the specified
categorical data set.
| |
Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) | |
Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) | |
GetHashCode | Serves as the default hash function. (Inherited from Object.) | |
GetType | Gets the Type of the current instance. (Inherited from Object.) | |
MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) | |
ToString | Returns a string that represents the current object. (Inherited from Object.) |
A multiple correspondence examines the relationships existing among the variables observed in a categorical data set. Both individuals and categories are represented as weighted points in a multidimensional space, having coordinates w.r.t. specific bases, forming what are referred to as clouds (For a definition, see the Cloud documentation). The aim of a multiple correspondence analysis is to project those clouds in a space having a lower dimensionality, in such a way that distances in space relate to dissimilarities among categories or among individuals.
Let be a categorical
data set, where individuals are
assigned to rows and variables to columns. Let be,
for ,
the domain of the -th categorical variable,
i.e. the set of its observed
categories in . Given the definition
one can map the overall observed categories to
indexes , so that
a disjunctive form of can be represented as
a matrix, say ,
such that, for and
,
if and only if the -th category has been observed
at the -th individual,
otherwise.
The number of individuals at which the -th category
has been observed
is thus given as .
Notice that matrix is a contingency table, so that the cloud of individuals and that of categories can be interpreted, respectively, as the clouds of row and column profiles taken into account while analyzing a correspondence. That is, a multiple correspondence analysis of consists precisely in the correspondence analysis of . As a consequence, in what follows is exploited the same notation used in the documentation of class Correspondence.
Cloud of individuals
Individuals are points in that correspond to the row profiles based on matrix . Their cloud can thus be obtained by specializing the cloud , as defined in the remarks about class Correspondence.
More thoroughly, the marginal relative frequency of the -th category
is equal to , hence
so that basis is representable
through the matrix
Furthermore, since variables are observed at each individual,
one also has, for ,
so that
and
where .
Cloud of categories
Categories are the column profiles in represented by the cloud , as discussed in the documentation of Correspondence.
In the current context, such cloud can be expressed as follows.
Basis has a representation matrix
evaluating to
while the point coordinates and weights are given by, respectively,
and, for ,
Principal projections
Information about the principal projections of categories and individuals are exposed through properties Categories and Individuals, respectively. These properties return objects of class PrincipalProjections. Check its documentation for a thorough explanation of the underlying statistical methods, and for a discussion about how to exploit the principal information of the clouds, or to locate supplementary points.