R/gt_pca.R

In tidypopgen: Tidy Population Genetics

#' Principal Component Analysis for `gen_tibble` objects
#'
#' There are a number of PCA methods available for `gen_tibble` objects. They
#' are mostly designed to work on very large datasets, so they only compute a
#' limited number of components. For smaller datasets, `gt_partialSVD` allows
#' the use of partial (truncated) SVD to fit the PCA; this method is suitable
#' when the number of individuals is much smaller than the number of loci. For
#' larger dataset, `gt_randomSVD` is more appropriate. Finally, there is a
#' method specifically designed for dealing with LD in large datasets,
#' `gt_autoSVD`. Whilst this is arguably the best option, it is somewhat data
#' hungry, and so only suitable for very large datasets (hundreds of individuals
#' with several hundred thousands markers, or larger).
#'
#' NOTE: using gt_pca_autoSVD with a small dataset will likely cause an error,
#' see man page for details.
#'
#' NOTE: monomorphic markers must be removed before PCA is computed. The error
#' message 'Error: some variables have zero scaling; remove them before
#' attempting to scale.' indicates that monomorphic markers are present.
#'
#' @name gt_pca
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