Chapter 3 Correspondence analysis
3.2 Review of Multiple Correspondence Analysis (MCA)
Suppose there are observations on categorical variables. Assume different values for variable . Next define a matrix, which is × matrix.
This matrix is known as indicator matrix. The × matrix , with the sum of can be obtained by concatenating the ’s (Greenacre, 1984). In general, MCA is defined as the application of weighted PCA to the indicator matrix (Benzécri, 1973). Furthermore, is divided by its grand total to obtain the correspondence matrix = , i.e., = 1, where is an × 1 vector of ones. The vectors = and = are the row and column marginals respectively. These marginals are the vectors of row and column masses.
Suppose the diagonal matrices of the masses are defined as !" = #$%() and
!& = #$%(). Note that, the ' element of is (. = and the )' element of is (.* = + where * is the frequency of category ) (Greenacre and Blasius, 2006).
MCA can be defined as the application of PCA to the centered matrix
!",( − ) with distances between profiles given by the chi-squared metric defined by !&,. The projected coordinate of the row profiles on the principal axes are called row principal coordinates. The × - matrix . of row principal coordinates is defined by
/ = !", ⁄ 123, (3.1)
31 where 1 = !", ⁄ ( − )!&, ⁄ and 23 is the × - matrix of eigenvectors corresponding to the - largest eigenvalues 4, . . . , 43 of the matrix 1 1. The projected row profiles can be plotted in the different planes defined by these principal axes called row principal planes (Greenacre and Blasius, 2006).
The categories for column profile can be described by the column profiles. The value can be calculated by dividing the columns of by their column marginals. Interchanging rows with columns and all associated entities can be used for the dual analysis of columns profiles. This is done by transposing the matrix and repeating all the steps. The metrics used to define the principal axes (weighted PCA) of the centered profiles matrix !&, ⁄ (6 − ) are !& and
!",.
The × - matrix 7 of columns principal coordinates is now defined by
7 = !&, ⁄ 183, (3.2)
where 83 is the × - matrix of eigenvectors corresponding to the - largest eigenvalues 4, . . . , 43 of the matrix 11. To aid visualization and interpretation of the projected column profiles in the planes defined by principal axes, which are called column principal planes, can be plotted (Johnson and Wichern, 2007).
The absolute contribution of the variable to the inertia of the column principal component 9 in the 9' column of 7 is given by
:; = < )=> (.*?*;
*∈AB
where > is the set of categories of variable . The relation between the absolute contribution :; and the correlation ratio between the variable and the row standard component 9 is given by
32 C; = < *
(D̅*;∗ − 0)
*∈AB
= × :;. (3.3)
Note that factor loadings for PCA are correlations between the variables and the components (the correlation ratios) are known as discrimination measures.
These values can be interpreted in MCA as squared loadings.
Suppose /H∗ = /∗I and 7H = 7I, where IIJ = I I = KL. Let /∗7J = /H∗7HJ. Then, these relations show that the lower rank approximation is not unique.
Furthermore, the MCA solutions /∗ and 7, are not unique over orthogonal rotations. The non-uniqueness can be explored to improve the interpretability of the original solution by means of rotation. Rotation of the column principal coordinates matrix 7 to simple structure must be followed by the same rotation of the row standard coordinates matrix /∗. The interpretation of the correlation ratios can be simplified for the matrices 7 and /∗ by rotation (Greenacre, 2000).
For the method of rotation, the Varimax based function can be used. After rotation of /∗ and 7, the relation (3.3) becomes
CM; = < (.*?M*;
*∈AB
, (3.4)
where CM; is the correlation ratio between the variable j and 9' column of OP∗. The graphical approach to represent the correspondence approach is the biplot representation. Therefore, biplot information is represented by × data matrix. As the name indicates, it refers to the two kinds of information contained in a data matrix. The information in the rows pertains to samples or sampling units and that in the columns pertains to variables. The scatter plot can represent the information on both the sampling units and the variables in a single diagram. This representation is useful to visualize the position of one sampling unit relative to another (Dray et al., 2003). In addition to this, it helps
33 to visualize the relative importance of each of the two variables to the position of any variables. Matrix array can be constructed with several variables using scatter plots. The idea behind biplots is to add the information about the variables to the graph. Therefore, the construction of a biplot leads the sample principal components and the best two-dimensional approximation to the data matrix / approximates the ' observation Q in terms of the sample values of the first two principal components. Specifically,
Q = /H + ?STS+ ?STS (3.5)
where TS and TS are the first two eigenvectors of U and equivalent to /&V/& = ( − 1)U and / denotes the mean correlated data matrix with rows WQX− /HYV. The eigenvectors determine a plane and the coordinates of the ' unit are the pair of values of the principal components (?S, ?S). The pair of eigenvectors has to be considered in order to include the information on the variables in the plot. These eigenvectors are coefficient vectors for the first two sample principal components. Thus, each row of the matrix positions (Z[ = \̂, ̂^) a variable in the graph and the magnitudes of the coordinates of the variables show the weightings of the variables. The weightings represent each principal component of the variables. The plots of the variable with corresponding position are indicated by a vector. Singular value decomposition is the direct approach to obtain a biplot. Then, the singular decomposition expresses the × mean correlated /& as
/&
( × ) = 8
( × ) ᴧ
( × ) 2V ( × )
where ᴧ = #$%(4, 4, . . . , 4) and 2 = Z[ = `TS, . . . , TSab is an orthogonal matrix whose columns are the eigenvector of /&V/& = ( − 1)U. The best rank two approximation to /& is obtained by replacing
ᴧ
by ᴧ ∗ = #$% (4, 4, 0, . . . ,0). Therefore, this result is known as Eckart-Young theorem. The approximation is given as34 /& = 8ᴧ∗2V= \cd, cde^ fTSTSVeVg (3.6)
where cd and cde are the × 1 vector of values for the first and second principal components respectively.
The biplot represents each row of the data matrix by the point located by the pair of values of the principal components. The ' column of the data matrix is represented as an arrow from the origin to the point with coordinates (, ), the entries in the ' column of the second matrix `TS, TSabV approximations.
Furthermore, the idea of a biplot extends to canonical correlation analysis, multidimensional scaling and even more complicated nonlinear techniques.