R/methods-mass-lda.r
methods-lda.RdThese methods extract data from, and attribute new data to,
objects of class "lda" and "lda_ord" as returned by MASS::lda() and
lda_ord().
# S3 method for lda
as_tbl_ord(x)
# S3 method for lda_ord
as_tbl_ord(x)
# S3 method for lda
recover_rows(x)
# S3 method for lda_ord
recover_rows(x)
# S3 method for lda
recover_cols(x)
# S3 method for lda_ord
recover_cols(x)
# S3 method for lda
recover_inertia(x)
# S3 method for lda_ord
recover_inertia(x)
# S3 method for lda
recover_coord(x)
# S3 method for lda_ord
recover_coord(x)
# S3 method for lda
recover_conference(x)
# S3 method for lda_ord
recover_conference(x)
# S3 method for lda
recover_aug_rows(x)
# S3 method for lda_ord
recover_aug_rows(x)
# S3 method for lda
recover_aug_cols(x)
# S3 method for lda_ord
recover_aug_cols(x)
# S3 method for lda
recover_aug_coord(x)
# S3 method for lda_ord
recover_aug_coord(x)
# S3 method for lda
recover_supp_rows(x)
# S3 method for lda_ord
recover_supp_rows(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.
See lda-ord for details.
Other methods for singular value decomposition-based techniques:
methods-cancor,
methods-correspondence,
methods-lra,
methods-mca,
methods-prcomp,
methods-princomp,
methods-svd
Other models from the MASS package:
methods-correspondence,
methods-mca
# 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
# default (unstandardized discriminant) coefficients
lda_ord(iris[, 1:4], iris[, 5]) %>%
as_tbl_ord() %>%
print() -> iris_lda
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 0 ]
#> LD1 LD2 |
#> |
#> 1 7.61 0.215 |
#> 2 -1.83 -0.728 |
#> 3 -5.78 0.513 |
#> 4 8.06 0.300 |
#> 5 7.13 -0.787 |
#>
#> #
#> # Columns (standard): [ 4 x 2 | 0 ]
#> LD1 LD2 |
#> |
#> 1 0.829 0.0241 |
#> 2 1.53 2.16 |
#> 3 -2.20 -0.932 |
#> 4 -2.81 2.84 |
# recover centroid coordinates and measurement discriminant coefficients
get_rows(iris_lda, elements = "active")
#> LD1 LD2
#> setosa 7.607600 0.2151330
#> versicolor -1.825049 -0.7278996
#> virginica -5.782550 0.5127666
head(get_rows(iris_lda, elements = "score"))
#> LD1 LD2
#> [1,] 8.061800 0.3004206
#> [2,] 7.128688 -0.7866604
#> [3,] 7.489828 -0.2653845
#> [4,] 6.813201 -0.6706311
#> [5,] 8.132309 0.5144625
#> [6,] 7.701947 1.4617210
get_cols(iris_lda)
#> LD1 LD2
#> Sepal.Length 0.8293776 0.02410215
#> Sepal.Width 1.5344731 2.16452123
#> Petal.Length -2.2012117 -0.93192121
#> Petal.Width -2.8104603 2.83918785
# augment ordination with centroid and measurement names
augment_ord(iris_lda)
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 5 ]
#> LD1 LD2 | name prior counts grouping
#> | <chr> <dbl> <int> <chr>
#> 1 7.61 0.215 | 1 setosa 0.333 50 setosa
#> 2 -1.83 -0.728 | 2 versicolor 0.333 50 versicol…
#> 3 -5.78 0.513 | 3 virginica 0.333 50 virginica
#> 4 8.06 0.300 | 4 NA NA NA setosa
#> 5 7.13 -0.787 | 5 NA NA NA setosa
#> # … with 148 more rows, and 1 more
#> # variable: .element <chr>
#> #
#> # Columns (standard): [ 4 x 2 | 2 ]
#> LD1 LD2 | name .element
#> | <chr> <chr>
#> 1 0.829 0.0241 | 1 Sepal.Length active
#> 2 1.53 2.16 | 2 Sepal.Width active
#> 3 -2.20 -0.932 | 3 Petal.Length active
#> 4 -2.81 2.84 | 4 Petal.Width active