Functionality for linear discriminant analysis ('lda') objects
Source: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().
Usage
# S3 method for class 'lda'
as_tbl_ord(x)
# S3 method for class 'lda_ord'
as_tbl_ord(x)
# S3 method for class 'lda'
recover_rows(x)
# S3 method for class 'lda_ord'
recover_rows(x)
# S3 method for class 'lda'
recover_cols(x)
# S3 method for class 'lda_ord'
recover_cols(x)
# S3 method for class 'lda'
recover_inertia(x)
# S3 method for class 'lda_ord'
recover_inertia(x)
# S3 method for class 'lda'
recover_coord(x)
# S3 method for class 'lda_ord'
recover_coord(x)
# S3 method for class 'lda'
recover_conference(x)
# S3 method for class 'lda_ord'
recover_conference(x)
# S3 method for class 'lda'
recover_aug_rows(x)
# S3 method for class 'lda_ord'
recover_aug_rows(x)
# S3 method for class 'lda'
recover_aug_cols(x)
# S3 method for class 'lda_ord'
recover_aug_cols(x)
# S3 method for class 'lda'
recover_aug_coord(x)
# S3 method for class 'lda_ord'
recover_aug_coord(x)
# S3 method for class 'lda'
recover_supp_rows(x)
# S3 method for class 'lda_ord'
recover_supp_rows(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
See lda-ord for details.
See also
Other methods for singular value decomposition-based techniques:
methods-cancor,
methods-correspondence,
methods-lra,
methods-mca,
methods-prcomp,
methods-svd
Other models from the MASS package:
methods-correspondence,
methods-mca
Examples
# 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 × 2) · (4 × 2)´
#> # 2 coordinates: LD1 and LD2
#> # Rows (principal, 100%): [ 153 × 2 | 0 ]
#> LD1 LD2 |
#> [2366] [20.98] |
#> 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, 0%): [ 4 × 2 | 0 ]
#> LD1 LD2 |
#> [1] [1] |
#> 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 × 2) · (4 × 2)´
#> # 2 coordinates: LD1 and LD2
#> # Rows (principal, 100%): [ 153 × 2 | 5 ]
#> LD1 LD2 | name prior counts grouping .element
#> [2366] [20.98] | <chr> <dbl> <int> <chr> <chr>
#> 1 7.61 -0.215 | setosa 0.333 50 setosa active
#> 2 -1.83 0.728 | versicolor 0.333 50 versicolor active
#> 3 -5.78 -0.513 | virginica 0.333 50 virginica active
#> 4 8.06 -0.300 | NA NA NA setosa score
#> 5 7.13 0.787 | NA NA NA setosa score
#> ⋮ ⋮
#> # Columns (standard, 0%): [ 4 × 2 | 2 ]
#> LD1 LD2 | name .element
#> [1] [1] | <chr> <chr>
#> 1 0.829 -0.0241 | Sepal.Length active
#> 2 1.53 -2.16 | Sepal.Width active
#> 3 -2.20 0.932 | Petal.Length active
#> 4 -2.81 -2.84 | Petal.Width active