percent_variance_explained

Specifies the number of components to retain to explain the specified percent variance.

Specification

• Alias: None

• Arguments: REAL

Description

Dakota can calculate the principal components of the response matrix of N samples * L responses using the keyword principal_components. Principal components analysis (PCA) is a data reduction method. percent_variance_explained is a threshold that determines the number of components that are retained to explain at least that amount of variance. For example, if the user specifies percent_variance_explained = 0.99, the number of components that accounts for at least 99 percent of the variance in the responses will be retained. The default for this percentage is 0.95. In many applications, only a few principal components explain the majority of the variance, resulting in significant data reduction.

Expected Outputs

Usage Tips percent_variance_explained should be a real number between 0.0 and 1.0. Typically, it will be between 0.9 and 1.0.

Examples

method,
  sampling
    sample_type lhs
    samples = 100
    principal_components
    percent_variance_explained = 0.98

Theory

There is an extensive statistical literature available on PCA. We recommend that the interested user peruse some of this in using the PCA capability.