This summarizes information about the components of a gt_pca from the
tidypopgen package. The parameter matrix determines which element is
returned. Column names of the tidied output match those returned by
broom::tidy.prcomp, the tidier for the standard PCA objects returned
by stats::prcomp.
Usage
# S3 method for class 'gt_pca'
tidy(x, matrix = "eigenvalues", ...)Arguments
- x
A
gt_pcaobject returned by one of thegt_pca_*functions.- matrix
Character specifying which component of the PCA should be tidied.
"samples","scores", or"x": returns information about the map from the original space into principle components space (this is equivalent to product of u and d)."v","rotation","loadings"or"variables": returns information about the map from principle components space back into the original space."d","eigenvalues"or"pcs": returns information about the eigenvalues.
- ...
Not used. Needed to match generic signature only.
Value
A tibble::tibble with columns depending on the component of PCA being tidied.
If "scores" each row in the
tidied output corresponds to the original data in PCA space. The columns
are:
rowID of the original observation (i.e. rowname from original data).
PCInteger indicating a principal component.
valueThe score of the observation for that particular principal component. That is, the location of the observation in PCA space.
If matrix is "loadings", each
row in the tidied output corresponds to information about the principle
components in the original space. The columns are:
rowThe variable labels (colnames) of the data set on which PCA was performed.
PCAn integer vector indicating the principal component.
valueThe value of the eigenvector (axis score) on the indicated principal component.
If "eigenvalues", the columns are:
PCAn integer vector indicating the principal component.
std.devStandard deviation (i.e. sqrt(eig/(n-1))) explained by this PC (for compatibility with
prcomp.cumulativeCumulative variation explained by principal components up to this component (note that this is NOT phrased as a percentage of total variance, since many methods only estimate a truncated SVD.
Examples
# Create a gen_tibble of lobster genotypes
bed_file <-
system.file("extdata", "lobster", "lobster.bed", package = "tidypopgen")
lobsters <- gen_tibble(bed_file,
backingfile = tempfile("lobsters"),
quiet = TRUE
)
# Remove monomorphic loci and impute
lobsters <- lobsters %>% select_loci_if(loci_maf(genotypes) > 0)
lobsters <- gt_impute_simple(lobsters, method = "mode")
# Create PCA object
pca <- gt_pca_partialSVD(lobsters)
# Tidy the PCA object
tidy(pca)
#> # A tibble: 10 × 4
#> PC std.dev percent cumulative
#> <int> <dbl> <dbl> <dbl>
#> 1 1 2.75 8.91 8.91
#> 2 2 2.32 6.37 15.3
#> 3 3 1.70 3.40 18.7
#> 4 4 1.56 2.89 21.6
#> 5 5 1.52 2.74 24.3
#> 6 6 1.52 2.73 27.0
#> 7 7 1.48 2.59 29.6
#> 8 8 1.46 2.52 32.1
#> 9 9 1.40 2.31 34.5
#> 10 10 1.38 2.25 36.7
# Tidy the PCA object for eigenvalues
tidy(pca, matrix = "eigenvalues")
#> # A tibble: 10 × 4
#> PC std.dev percent cumulative
#> <int> <dbl> <dbl> <dbl>
#> 1 1 2.75 8.91 8.91
#> 2 2 2.32 6.37 15.3
#> 3 3 1.70 3.40 18.7
#> 4 4 1.56 2.89 21.6
#> 5 5 1.52 2.74 24.3
#> 6 6 1.52 2.73 27.0
#> 7 7 1.48 2.59 29.6
#> 8 8 1.46 2.52 32.1
#> 9 9 1.40 2.31 34.5
#> 10 10 1.38 2.25 36.7
# Tidy the PCA object for loadings
tidy(pca, matrix = "loadings")
#> # A tibble: 790 × 3
#> column PC value
#> <chr> <int> <dbl>
#> 1 rs3441 1 0.0464
#> 2 rs3441 2 -0.00611
#> 3 rs3441 3 -0.0927
#> 4 rs3441 4 0.131
#> 5 rs3441 5 0.188
#> 6 rs3441 6 0.0378
#> 7 rs3441 7 0.136
#> 8 rs3441 8 0.0756
#> 9 rs3441 9 -0.107
#> 10 rs3441 10 -0.0705
#> # ℹ 780 more rows
# Tidy the PCA object for scores
tidy(pca, matrix = "scores")
#> # A tibble: 1,760 × 3
#> row PC value
#> <chr> <int> <dbl>
#> 1 Ale04 1 3.43
#> 2 Ale04 2 -2.93
#> 3 Ale04 3 1.96
#> 4 Ale04 4 0.103
#> 5 Ale04 5 -1.83
#> 6 Ale04 6 1.01
#> 7 Ale04 7 -1.25
#> 8 Ale04 8 0.212
#> 9 Ale04 9 0.199
#> 10 Ale04 10 1.17
#> # ℹ 1,750 more rows