pca: Run PCA on Genotype Table
In maize-genetics/rTASSEL: R Front-End for TASSEL

View source: R/analysis_relatedness_functions.R

pca	R Documentation

Run PCA on Genotype Table

Description

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.