Predict scores of a PCA — predict.gt

Predict the PCA scores for a gt_pca, either for the original data or projecting new data.

Usage

# S3 method for class 'gt_pca'
predict(
  object,
  new_data = NULL,
  project_method = c("none", "simple", "OADP", "least_squares"),
  lsq_pcs = c(1, 2),
  block_size = NULL,
  n_cores = 1,
  ...
)

Arguments

object: the gt_pca object
new_data: a gen_tibble if scores are requested for a new dataset
project_method: a string taking the value of either "simple", "OADP" (Online Augmentation, Decomposition, and Procrustes (OADP) projection), or "least_squares" (as done by SMARTPCA)
lsq_pcs: a vector of length two with the values of the two principal components to use for the least square fitting. Only relevant ifproject_method = 'least_squares'
block_size: number of loci read simultaneously (larger values will speed up computation, but require more memory)
n_cores: number of cores
...: no used

Value

a matrix of predictions, with samples as rows and components as columns. The number of components depends on how many were estimated in the gt_pca object.

References

Zhang et al (2020). Fast and robust ancestry prediction using principal component analysis 36(11): 3439–3446.