correlation matrix

A g factor can be computed from a correlation matrix of test results using several different methods.

Obtaining the squared multiple correlations from a singular correlation matrix.

Orthogonality is guaranteed by the symmetry of the correlation matrices.

When the covariance is normalized, one obtains the correlation matrix.

Another good example is random matrix theory, which can be used to identify the noise in financial correlation matrices.

When factoring a correlation matrix, the estimated answers appear as the first set of factor scores.

On the basis of the correlation matrix, one would expect to identify three tumor classes in this dataset.

The purpose is to simplify the correlation matrix by using hypothetical underlying factors to explain the patterns in it.

Compute the eigenvalues for the correlation matrix and plot the values from largest to smallest.

The correlation matrices were thus transformed into linear probability matrices.

We provide detailed correlation matrices that can be used to detect and control for non-syn-tenic association.

This may partly account for the clear separation of the classes even when the correlation matrix is computed using the full set of genes.

In practice, the correlation matrix of the data is constructed and the eigenvectors on this matrix are computed.

Factor analyses of three correlation matrices of readability variables.

Compute the eigenvalues for the correlation matrix and determine how many of these eigenvalues are greater than 1.

Leukemia Images of the correlation matrix for the leukemia dataset are displayed in Figure 8.

CCA can be computed using singular value decomposition on a correlation matrix.

The g factor can be derived from a correlation matrix of mental ability tests by many different methods of factor analysis.

This point is exemplified by Brown (2009), who indicated that, in respect to the correlation matrices involved in the calculations:

Technology of improving the stipulation of correlation matrices with balancing relative errors of their elements.

The mean, standard deviation, variance-covariance matrix, and correlation matrix are calculated from the measurement vectors.

Given two groups, a correlation matrix (m by m) was constructed for m variables for each group.

Instead of specifying a correlation matrix, an OR is computed for each cluster in this approach to detect clustering among related observations.

Step 3: Compute the cross correlation for all the stocks and create a cross correlation matrix .

Step 4: In case of the minimum spanning tree method a metric distance [1] is calculated using the cross correlation matrix.