Contributions: This table displays the contributions that help evaluate how much an object contributes to a given axis.Principal coordinates: This table displays the principal coordinates of the objects that are used to create the chart where the proximities between the charts can be interpreted.Eigenvalues and percentage of inertia: this table displays the eigenvalues and the corresponding percentage of inertia.Delta1 matrix: This matrix corresponds to the D1 matrix of Gower, used to compute the eigen-decomposition.Results of Principal Coordinate Analysis in XLSTAT , p-1 dimensions.Īs with PCA ( Principal Component Analysis) eigenvalues can be interpreted in terms of percentage of total variability that is being represented in a reduced space. The rescaled eigenvectors correspond to the principal coordinates (principal axes), which are synthetic variables, that can be used to display the p objects in a space with 1, 2. Eigen-decomposition of the centered distance matrix.Centering of the matrix by rows and columns.a euclidean distance matrix, for the p elements Computation of a distance matrix, e.g.The algorithm can be divided into three steps: Principal Coordinate Analysis (often referred to as PCoA) is aimed at graphically representing a resemblance matrix (similarity matrix or dissimilarity matrix) between p elements (individuals, variables, objects, among others). Filter factors by fixing a maximum number of axes to be retained or by fixing a minimum of variance explained.Correct negative eigenvalues if needed using the Square root or Lingoes correction.Run a PCoA on a similarity or a dissimilarity matrix.XLSTAT provides a PCoA feature with several standard options that will let you represent your data efficiently and gain a deep insight on them: a euclidean distance matrix, or a similarity matrix, e.g. Principal Coordinate Analysis ( PCoA) is a powerful and popular multivariate analysis method that lets you analyze a proximity matrix, whether it is a dissimilarity matrix, e.g.
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