Run PCA on Genotype TableSource:
This method performs principal components analysis and returns the requested number of PC axes (components).
pca( tasObj, useCovariance = TRUE, limitBy = c("number_of_components", "min_eigenvalue", "total_variance"), nComponents = 5, minEigenval = 0, totalVar = 0.5, reportEigenvalues = TRUE, reportEigenvectors = TRUE )
TRUE, analysis will do an eigenvalue decomposition of the covariance matrix. If
FALSE, it will use a correlation matrix. NOTE: Using the covariance matrix is recommended for genotypes while the correlation matrix is often used for phenotypes. Defaults to
This parameter determines the type of value that will be used to limit the number of principal components (axes) returned. The possible choices are
The analysis will return this many principal components up to the number of taxa.
All principal components with an eigenvalue greater than or equal to this value will be returned. NOTE: works only if
min_eigenvalueis set in the
The first principal components that together explain this proportion of the total variance will be returned. NOTE: works only if
total_varianceis set in the
Returns a list of eigenvalues sorted high to low.
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.