cda-class: Canonical Disciminant Analysis model
In fdm2id: Data Mining and R Programming for Beginners

cda-class

R Documentation

Canonical Disciminant Analysis model

This class contains the classification model obtained by the CDA method.

Objects of this class are plain lists with the following components:

proj: The projection of the dataset into the canonical base. A data.frame.
transform: The transformation matrix between. A matrix.
centers: Coordinates of the class centers. A matrix.
within: The intra-class covariance matrix. A matrix.
eig: One row per canonical axis and four columns: the eigenvalue (of V^{-1}B, i.e. the squared canonical correlation, between 0 and 1), its percentage of variance (share of the trace) and the cumulative one, and the discriminant power – the share of the trace of W^{-1}B, which is what lda and most other software call the proportion of trace. A matrix, or a named vector when there is a single axis.
dim: The number of dimensions of the canonical base (numeric value).
nb.classes: The number of clusters (numeric value).
train: The training set (description). A data.frame.
labels: Class labels of the training set. Either a factor or an integer vector.
model: The prediction model.