This method performs principal components analysis and returns the requested number of PC axes (components).

## Usage

pca(
tasObj,
useCovariance = TRUE,
limitBy = c("number_of_components", "min_eigenvalue", "total_variance"),
nComponents = 5,
minEigenval = 0,
totalVar = 0.5,
reportEigenvalues = TRUE,
reportEigenvectors = TRUE
)

## Arguments

tasObj

an rTASSEL TasselGenotypePhenotype object.

useCovariance

If 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 TRUE.

limitBy

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.

nComponents

The analysis will return this many principal components up to the number of taxa.

minEigenval

All principal components with an eigenvalue greater than or equal to this value will be returned. NOTE: works only if min_eigenvalue is set in the limitBy parameter.

totalVar

The first principal components that together explain this proportion of the total variance will be returned. NOTE: works only if total_variance is set in the limitBy parameter.

reportEigenvalues

Returns a list of eigenvalues sorted high to low.

reportEigenvectors

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

A DataFrame object.