mapping_key() returns a data frame with each reduced variable and its
mapping and information loss; the mapping and indices are represented as
list-cols (so there is one row per variable in the reduced data set).
unnest_mappings() unnests the list columns to return a tidy data frame.
mapping_groups() returns a list of mappings (either the variable names or
their column position).
mapping_key(.partition)
unnest_mappings(.partition)
mapping_groups(.partition, indices = FALSE)a tibble
set.seed(123)
df <- simulate_block_data(c(3, 4, 5), lower_corr = .4, upper_corr = .6, n = 100)
# fit partition
prt <- partition(df, threshold = .6)
# tibble: 6 x 4
mapping_key(prt)
#> # A tibble: 9 × 4
#> variable mapping information indices
#> <chr> <list> <dbl> <list>
#> 1 block1_x1 <chr [1]> 1 <int [1]>
#> 2 block1_x2 <chr [1]> 1 <int [1]>
#> 3 block1_x3 <chr [1]> 1 <int [1]>
#> 4 block2_x4 <chr [1]> 1 <int [1]>
#> 5 block3_x2 <chr [1]> 1 <int [1]>
#> 6 block3_x3 <chr [1]> 1 <int [1]>
#> 7 block3_x4 <chr [1]> 1 <int [1]>
#> 8 reduced_var_1 <chr [2]> 0.656 <int [2]>
#> 9 reduced_var_2 <chr [3]> 0.627 <int [3]>
# tibble: 12 x 4
unnest_mappings(prt)
#> # A tibble: 12 × 4
#> variable mapping information indices
#> <chr> <chr> <dbl> <int>
#> 1 block1_x1 block1_x1 1 1
#> 2 block1_x2 block1_x2 1 2
#> 3 block1_x3 block1_x3 1 3
#> 4 block2_x4 block2_x4 1 7
#> 5 block3_x2 block3_x2 1 9
#> 6 block3_x3 block3_x3 1 10
#> 7 block3_x4 block3_x4 1 11
#> 8 reduced_var_1 block3_x1 0.656 8
#> 9 reduced_var_1 block3_x5 0.656 12
#> 10 reduced_var_2 block2_x1 0.627 4
#> 11 reduced_var_2 block2_x2 0.627 5
#> 12 reduced_var_2 block2_x3 0.627 6
# list: length 6
mapping_groups(prt)
#> [[1]]
#> [1] "block1_x1"
#>
#> [[2]]
#> [1] "block1_x2"
#>
#> [[3]]
#> [1] "block1_x3"
#>
#> [[4]]
#> [1] "block2_x4"
#>
#> [[5]]
#> [1] "block3_x2"
#>
#> [[6]]
#> [1] "block3_x3"
#>
#> [[7]]
#> [1] "block3_x4"
#>
#> [[8]]
#> [1] "block3_x1" "block3_x5"
#>
#> [[9]]
#> [1] "block2_x1" "block2_x2" "block2_x3"
#>