gen_tibble objects by partial SVDgt_pca_partialSVD.RdThis function performs Principal Component Analysis on a gen_tibble,
by partial SVD through the eigen decomposition of the covariance. It works well
if the number of individuals is much smaller than the number of loci; otherwise,
gt_pca_randomSVD() is a better option. This function is a wrapper
for bigstatsr::big_SVD().
gt_pca_partialSVD(x, k = 10, fun_scaling = bigsnpr::snp_scaleBinom())a gen_tbl object
Number of singular vectors/values to compute. Default is 10.
This algorithm should be used to compute a few singular vectors/values.
Usually this can be left unset, as it defaults to
bigsnpr::snp_scaleBinom(), which is the appropriate function for biallelic SNPs.
Alternatively it is possible to use custom function
(see bigsnpr::snp_autoSVD() for details.
a gt_pca object, which is a subclass of bigSVD; this is
an S3 list with elements:
A named list (an S3 class "big_SVD") of
d, the eigenvalues (singular values, i.e. as variances),
u, the scores for each sample on each component (the left singular vectors)
v, the loadings (the right singular vectors)
center, the centering vector,
scale, the scaling vector,
method, a string defining the method (in this case 'partialSVD'),
call, the call that generated the object.
Note: rather than accessing these elements directly, it is better to use
tidy and augment. See gt_pca_tidiers.