R/methods-stats-prcomp.r
methods-prcomp.RdThese methods extract data from, and attribute new data to,
objects of class "prcomp" as returned by stats::prcomp().
# S3 method for prcomp
as_tbl_ord(x)
# S3 method for prcomp
recover_rows(x)
# S3 method for prcomp
recover_cols(x)
# S3 method for prcomp
recover_inertia(x)
# S3 method for prcomp
recover_coord(x)
# S3 method for prcomp
recover_conference(x)
# S3 method for prcomp
recover_aug_rows(x)
# S3 method for prcomp
recover_aug_cols(x)
# S3 method for prcomp
recover_aug_coord(x)An ordination object.
The recovery generics recover_*() return core model components, distribution of inertia,
supplementary elements, and intrinsic metadata; but they require methods for each model class to
tell them what these components are.
The generic as_tbl_ord() returns its input wrapped in the 'tbl_ord'
class. Its methods determine what model classes it is allowed to wrap. It
then provides 'tbl_ord' methods with access to the recoverers and hence to
the model components.
Other methods for singular value decomposition-based techniques:
methods-cancor,
methods-correspondence,
methods-lda,
methods-lra,
methods-mca,
methods-princomp,
methods-svd
Other models from the stats package:
methods-cancor,
methods-cmds,
methods-factanal,
methods-kmeans,
methods-lm,
methods-princomp
# data frame of Anderson iris species measurements
class(iris)
#> [1] "data.frame"
head(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
# compute scaled row-principal components of scaled measurements
iris[, -5] %>%
prcomp(scale = TRUE) %>%
as_tbl_ord() %>%
print() -> iris_pca
#> # A tbl_ord of class 'prcomp': (150 x 4) x (4 x 4)'
#> # 4 coordinates: PC1, PC2, ..., PC4
#> #
#> # Rows (principal): [ 150 x 4 | 0 ]
#> PC1 PC2 PC3 ... |
#> |
#> 1 -2.26 -0.478 0.127 |
#> 2 -2.07 0.672 0.234 ... |
#> 3 -2.36 0.341 -0.0441 |
#> 4 -2.29 0.595 -0.0910 |
#> 5 -2.38 -0.645 -0.0157 |
#>
#> #
#> # Columns (standard): [ 4 x 4 | 0 ]
#> PC1 PC2 PC3 ... |
#> |
#> 1 0.521 -0.377 0.720 |
#> 2 -0.269 -0.923 -0.244 ... |
#> 3 0.580 -0.0245 -0.142 |
#> 4 0.565 -0.0669 -0.634 |
# recover observation principal coordinates and measurement standard coordinates
head(get_rows(iris_pca))
#> PC1 PC2 PC3 PC4
#> [1,] -2.257141 -0.4784238 0.12727962 0.024087508
#> [2,] -2.074013 0.6718827 0.23382552 0.102662845
#> [3,] -2.356335 0.3407664 -0.04405390 0.028282305
#> [4,] -2.291707 0.5953999 -0.09098530 -0.065735340
#> [5,] -2.381863 -0.6446757 -0.01568565 -0.035802870
#> [6,] -2.068701 -1.4842053 -0.02687825 0.006586116
get_cols(iris_pca)
#> PC1 PC2 PC3 PC4
#> Sepal.Length 0.5210659 -0.37741762 0.7195664 0.2612863
#> Sepal.Width -0.2693474 -0.92329566 -0.2443818 -0.1235096
#> Petal.Length 0.5804131 -0.02449161 -0.1421264 -0.8014492
#> Petal.Width 0.5648565 -0.06694199 -0.6342727 0.5235971
# augment measurements with names and scaling parameters
(iris_pca <- augment_ord(iris_pca))
#> # A tbl_ord of class 'prcomp': (150 x 4) x (4 x 4)'
#> # 4 coordinates: PC1, PC2, ..., PC4
#> #
#> # Rows (principal): [ 150 x 4 | 0 ]
#> PC1 PC2 PC3 ... |
#> |
#> 1 -2.26 -0.478 0.127 |
#> 2 -2.07 0.672 0.234 ... |
#> 3 -2.36 0.341 -0.0441 |
#> 4 -2.29 0.595 -0.0910 |
#> 5 -2.38 -0.645 -0.0157 |
#>
#> #
#> # Columns (standard): [ 4 x 4 | 3 ]
#> PC1 PC2 PC3 ... | name center scale
#> | <chr> <dbl> <dbl>
#> 1 0.521 -0.377 0.720 | 1 Sepal.Length 5.84 0.828
#> 2 -0.269 -0.923 -0.244 ... | 2 Sepal.Width 3.06 0.436
#> 3 0.580 -0.0245 -0.142 | 3 Petal.Length 3.76 1.77
#> 4 0.565 -0.0669 -0.634 | 4 Petal.Width 1.20 0.762