Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Specifies the number of components to retain to explain the specified percent variance.
Alias: none
Argument(s): REAL
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.
method, sampling sample_type lhs samples = 100 principal_components percent_variance_explained = 0.98
There is an extensive statistical literature available on PCA. We recommend that the interested user peruse some of this in using the PCA capability.