public class PrincipalComponentsPlugin
extends AbstractPlugin
public PrincipalComponentsPlugin(java.awt.Frame parentFrame,
boolean isInteractive)
public java.lang.String pluginDescription()
public javax.swing.ImageIcon getIcon()
public java.lang.String getButtonName()
public java.lang.String getToolTipText()
public Phenotype runPlugin(DataSet input)
Convenience method to run plugin with one return object.
public java.lang.Boolean covariance()
If the box is checked, then the analysis will do an eigenvalue decomposition of the covariance matrix. If the box is unchecked, it will use a correlation matrix. Using the covariance matrix is recommended for genotypes while the correlation matrix is often used for phenotypes.
public PrincipalComponentsPlugin covariance(java.lang.Boolean value)
Set covariance (alternative = correlation). If the box is checked, then the analysis will do an eigenvalue decomposition of the covariance matrix. If the box is unchecked, it will use a correlation matrix. Using the covariance matrix is recommended for genotypes while the correlation matrix is often used for phenotypes.
value - covariance (alternative = correlation)public net.maizegenetics.analysis.data.PrincipalComponentsPlugin.PCA_LIMIT limitNumberOfComponentsBy()
This parameter determines the type of value that will be used to limit the number of principal components (axes) returned. The possible choices are number_of_components, min_eigenvalue, and total_variance.
public PrincipalComponentsPlugin limitNumberOfComponentsBy(net.maizegenetics.analysis.data.PrincipalComponentsPlugin.PCA_LIMIT value)
Set limit number of components by. This parameter determines the type of value that will be used to limit the number of principal components (axes) returned. The possible choices are number_of_components, min_eigenvalue, and total_variance.
value - limit number of components bypublic java.lang.Integer numberOfComponents()
The analysis will return this many principal components up to the number of taxa.
public PrincipalComponentsPlugin numberOfComponents(java.lang.Integer value)
Set number of components. The analysis will return this many principal components up to the number of taxa.
value - number of componentspublic java.lang.Double minimumEigenvalue()
All principal components with an eigenvalue greater than or equal to this value will be returned.
public PrincipalComponentsPlugin minimumEigenvalue(java.lang.Double value)
Set minimum eigenvalue. All principal components with an eigenvalue greater than or equal to this value will be returned.
value - minimum eigenvaluepublic java.lang.Double totalVariance()
The first principal components that together explain this proportion of the total variance will be returned.
public PrincipalComponentsPlugin totalVariance(java.lang.Double value)
Set total variance. The first principal components that together explain this proportion of the total variance will be returned.
value - total variancepublic java.lang.Boolean returnEigenvalues()
Returns a list of eigenvalues sorted high to low.
public PrincipalComponentsPlugin returnEigenvalues(java.lang.Boolean value)
Set Return Eigenvalues. Returns a list of eigenvalues sorted high to low.
value - Return Eigenvaluespublic java.lang.Boolean returnEigenvectors()
Returns the eigenvectors calculated from a Singular Value Decomposition of the data. The resulting table can be quite large if the number of variants and taxa are big.
public PrincipalComponentsPlugin returnEigenvectors(java.lang.Boolean value)
Set Return Eigenvectors. Returns the eigenvectors calculated from a Singular Value Decomposition of the data. The resulting table can be quite large if the number of variants and taxa are big.
value - Return Eigenvectors