Creates a deep copy of this principal component analysis.

Determines whether the specified Object is equal to the current Object.

Serves as a hash function for a particular type.

Gets the Type of the current instance.

Gets the number of principal components required to account for the given proportion of the total variance.

Returns a String that represents the current Object.

Gets the cumulative variance proportions.

Gets the data matrix.

Gets the eigenvalues of the covariance/correlation matrix, though the calculation is actually performed using the singular values of the data matrix.

Returns true if the data supplied at construction time was shifted to be zero-centered.

Returns true if the data supplied at construction time was scaled to have unit variance.

Gets the specified principal component.

Gets the loading matrix. Each column is a principal component.

Gets the column means of the data matrix.

Gets the column norms (1-norm).

Gets the number of observations in the data matrix.

Gets the number of variables in the data matrix.

Gets the score matrix.

Gets the standard deviations of the principal components.

Gets the proportion of the total variance accounted for by each principal component.