These methods extract data from, and attribute new data to,
objects of class "eigen" returned by base::eigen() when the parameter
only.values is set to FALSE or of class "eigen_ord" returned by
eigen_ord().
Usage
# S3 method for class 'eigen'
as_tbl_ord(x)
# S3 method for class 'eigen'
recover_rows(x)
# S3 method for class 'eigen'
recover_cols(x)
# S3 method for class 'eigen'
recover_inertia(x)
# S3 method for class 'eigen'
recover_coord(x)
# S3 method for class 'eigen'
recover_conference(x)
# S3 method for class 'eigen'
recover_aug_rows(x)
# S3 method for class 'eigen'
recover_aug_cols(x)
# S3 method for class 'eigen'
recover_aug_coord(x)
# S3 method for class 'eigen_ord'
as_tbl_ord(x)
# S3 method for class 'eigen_ord'
recover_rows(x)
# S3 method for class 'eigen_ord'
recover_cols(x)
# S3 method for class 'eigen_ord'
recover_inertia(x)
# S3 method for class 'eigen_ord'
recover_coord(x)
# S3 method for class 'eigen_ord'
recover_conference(x)
# S3 method for class 'eigen_ord'
recover_aug_rows(x)
# S3 method for class 'eigen_ord'
recover_aug_cols(x)
# S3 method for class 'eigen_ord'
recover_aug_coord(x)Value
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.
Details
base::eigen() usually returns an object of class "eigen", which contains
the numerical eigendecomposition without annotations such as row and column
names. To facilitate downstream analysis, eigen_ord() returns a modified
'eigen' object with row names taken (if available) from the original data and
column names indicating the integer index of each eigenvector.
See also
Other methods for eigen-decomposition-based techniques:
methods-cmds,
methods-factanal,
methods-princomp
Other models from the base package:
methods-svd
Examples
# eigendecompose covariance matrix of ability tests
gi_eigen <- eigen(ability.cov$cov)
# recover eigenvectors
get_rows(gi_eigen)
#> EV1 EV2 EV3 EV4 EV5 EV6
#> [1,] -0.22869031 -0.02614951 0.55367919 0.65291077 -0.42125795 -0.191586979
#> [2,] -0.08380792 -0.08365448 0.08175861 0.04179166 -0.21588953 0.964851729
#> [3,] -0.62773627 -0.73872033 -0.12880737 -0.18084446 -0.01592981 -0.103390720
#> [4,] -0.09460078 -0.08807900 -0.01657141 0.54566079 0.81513697 0.144305927
#> [5,] -0.36706353 0.30009148 0.67111717 -0.47373359 0.31556525 0.028394942
#> [6,] -0.63477493 0.59059089 -0.46849833 0.13071747 -0.10793510 0.005954517
identical(get_cols(gi_eigen), get_rows(gi_eigen))
#> [1] TRUE
# wrap as a 'tbl_ord'
as_tbl_ord(gi_eigen)
#> # A tbl_ord of class 'eigen': (6 × 6) · (6 × 6)´
#> # 6 coordinates: EV1, EV2, ..., EV6
#> # Rows (standard, 0%): [ 6 × 6 | 0 ]
#> EV1 EV2 EV3 EV4 EV5 EV6 |
#> [1] [1] [1] [1] [1] [1] |
#> 1 -0.229 -0.0261 0.554 0.653 -0.421 -0.192 |
#> 2 -0.0838 -0.0837 0.0818 0.0418 -0.216 0.965 |
#> 3 -0.628 -0.739 -0.129 -0.181 -0.0159 -0.103 |
#> 4 -0.0946 -0.0881 -0.0166 0.546 0.815 0.144 |
#> 5 -0.367 0.300 0.671 -0.474 0.316 0.0284 |
#> 6 -0.635 0.591 -0.468 0.131 -0.108 0.00595 |
#> # Columns (standard, 0%): [ 6 × 6 | 0 ]
#> EV1 EV2 EV3 EV4 EV5 EV6 |
#> [1] [1] [1] [1] [1] [1] |
#> 1 -0.229 -0.0261 0.554 0.653 -0.421 -0.192 |
#> 2 -0.0838 -0.0837 0.0818 0.0418 -0.216 0.965 |
#> 3 -0.628 -0.739 -0.129 -0.181 -0.0159 -0.103 |
#> 4 -0.0946 -0.0881 -0.0166 0.546 0.815 0.144 |
#> 5 -0.367 0.300 0.671 -0.474 0.316 0.0284 |
#> 6 -0.635 0.591 -0.468 0.131 -0.108 0.00595 |
# same eigendecomposition, preserving names
gi_eigen <- eigen_ord(ability.cov$cov)
#> Warning: `eigen_ord()` is deprecated in favor of `base::eigen()`.
#> This warning is displayed once per session.
# wrap as a 'tbl_ord' and augment with dimension names
augment_ord(as_tbl_ord(gi_eigen))
#> # A tbl_ord of class 'eigen_ord': (6 × 6) · (6 × 6)´
#> # 6 coordinates: EV1, EV2, ..., EV6
#> # Rows (standard, 0%): [ 6 × 6 | 2 ]
#> EV1 EV2 EV3 EV4 EV5 | name .element
#> [1] [1] [1] [1] [1] | <chr> <chr>
#> 1 -0.229 -0.0261 0.554 0.653 -0.421 | general active
#> 2 -0.0838 -0.0837 0.0818 0.0418 -0.216 | picture active
#> 3 -0.628 -0.739 -0.129 -0.181 -0.0159 | blocks active
#> 4 -0.0946 -0.0881 -0.0166 0.546 0.815 | maze active
#> 5 -0.367 0.300 0.671 -0.474 0.316 | reading active
#> 6 -0.635 0.591 -0.468 0.131 -0.108 | vocab active
#> # Columns (standard, 0%): [ 6 × 6 | 2 ]
#> EV1 EV2 EV3 EV4 EV5 | name .element
#> [1] [1] [1] [1] [1] | <chr> <chr>
#> 1 -0.229 -0.0261 0.554 0.653 -0.421 | general active
#> 2 -0.0838 -0.0837 0.0818 0.0418 -0.216 | picture active
#> 3 -0.628 -0.739 -0.129 -0.181 -0.0159 | blocks active
#> 4 -0.0946 -0.0881 -0.0166 0.546 0.815 | maze active
#> 5 -0.367 0.300 0.671 -0.474 0.316 | reading active
#> 6 -0.635 0.591 -0.468 0.131 -0.108 | vocab active
# decomposition returns pure eigenvectors
get_conference(gi_eigen)
#> [1] 0 0