Functions to get components from the data collecting object
Usage
get_best(dt, group = NULL)
get_start(dt, group = NULL)
get_interp(dt, group = NULL)
get_interp_last(dt, group = NULL)
get_anchor(dt, group = NULL)
get_search(dt)
get_dir_search(dt, ratio = 5, ...)
get_space_param(dt, ...)
get_theo(dt)
get_interrupt(dt, group = NULL, precision = 0.001)
get_search_count(dt, iter = NULL, group = NULL)
get_basis_matrix(dt)Arguments
- dt
a data object collected by the projection pursuit guided tour optimisation in the
tourrpackage- group
the variable to label different runs of the optimiser(s)
- ratio
numeric; a buffer value to deviate directional search points from the anchor points
- ...
other arguments passed to
compute_pca()- precision
numeric; if the index value of the last interpolating point and the anchor point differ by
precision, an interruption is registered- iter
the variable to be counted by
Details
get_best: extract the best basis found by the optimiser(s)
get_start: extract the start point of the optimisation
get_interp: extract the interpolation points
get_interp_last: extract the last point in each interpolation
get_anchor: extract the anchor points on the geodesic path
get_search: extract search points in the optimisation (for
search_geodesic)
get_dir_search: extract directional search points (for
search_geodesic)
get_space_param: estimate the radius of the background circle
based on the randomly generated points. The space of projected bases is a
circle when reduced to 2D. A radius is estimated using the largest distance
from the bases in the data object to the centre point.
get_theo: extract the theoretical basis, if exist
get_interrupt: extract the end point of the interpolation and the
target point in the iteration when an interruption happens. The optimiser
can find better basis on the interpolation path, an interruption is
implemented to stop further interpolation from the highest point to the
target point. This discrepancy is highlighted in the PCA plot.
get_search_count: summarise the number of search points in each iteration
get_basis_matrix: extract all the bases as a matrix
Examples
get_search(holes_1d_geo)
#> # A tibble: 363 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 <dbl [5 × 1]> 0.749 direction_search search… NA 2 1 2
#> 2 <dbl [5 × 1]> 0.749 direction_search search… NA 2 1 3
#> 3 <dbl [5 × 1]> 0.749 direction_search search… NA 2 1 4
#> 4 <dbl [5 × 1]> 0.749 direction_search search… NA 2 1 5
#> 5 <dbl [5 × 1]> 0.749 direction_search search… NA 2 1 6
#> 6 <dbl [5 × 1]> 0.749 direction_search search… NA 2 1 7
#> 7 <dbl [5 × 1]> 0.749 direction_search search… NA 2 1 8
#> 8 <dbl [5 × 1]> 0.749 direction_search search… NA 2 1 9
#> 9 <dbl [5 × 1]> 0.749 best_direction_search search… NA 2 1 10
#> 10 <dbl [5 × 1]> 0.749 direction_search search… NA 2 1 11
#> # ℹ 353 more rows
get_anchor(holes_1d_geo)
#> # A tibble: 34 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 <dbl [5 × 1]> 0.749 new_basis search_geod… 0.5 1 1 1
#> 2 <dbl [5 × 1]> 0.753 best_line_search search_geod… NA 2 1 2
#> 3 <dbl [5 × 1]> 0.793 best_line_search search_geod… NA 3 1 3
#> 4 <dbl [5 × 1]> 0.805 best_line_search search_geod… NA 4 1 4
#> 5 <dbl [5 × 1]> 0.836 best_line_search search_geod… NA 5 1 5
#> 6 <dbl [5 × 1]> 0.890 best_line_search search_geod… NA 6 1 6
#> 7 <dbl [5 × 1]> 0.917 best_line_search search_geod… NA 7 1 7
#> 8 <dbl [5 × 1]> 0.929 best_line_search search_geod… NA 8 1 8
#> 9 <dbl [5 × 1]> 0.929 best_line_search search_geod… NA 9 1 9
#> 10 <dbl [5 × 1]> 0.933 best_line_search search_geod… NA 9 2 10
#> # ℹ 24 more rows
get_start(holes_1d_better)
#> # A tibble: 1 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 <dbl [5 × 1]> 0.749 new_basis search_better 0.5 1 1 1
get_interrupt(holes_1d_better)
#> # A tibble: 6 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 <dbl [5 × 1]> 0.752 new_basis search_better 0.5 2 6 2
#> 2 <dbl [5 × 1]> 0.813 new_basis search_better 0.49 4 7 4
#> 3 <dbl [5 × 1]> 0.904 new_basis search_better 0.485 5 3 5
#> 4 <dbl [5 × 1]> 0.753 interpolation search_better NA 2 9 2
#> 5 <dbl [5 × 1]> 0.866 interpolation search_better NA 4 7 4
#> 6 <dbl [5 × 1]> 0.914 interpolation search_better NA 5 6 5
get_interp(holes_1d_better) %>% head()
#> # A tibble: 6 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 <dbl [5 × 1]> 0.749 interpolation search_better NA 2 1 1
#> 2 <dbl [5 × 1]> 0.750 interpolation search_better NA 2 2 2
#> 3 <dbl [5 × 1]> 0.751 interpolation search_better NA 2 3 3
#> 4 <dbl [5 × 1]> 0.751 interpolation search_better NA 2 4 4
#> 5 <dbl [5 × 1]> 0.752 interpolation search_better NA 2 5 5
#> 6 <dbl [5 × 1]> 0.752 interpolation search_better NA 2 6 6
get_basis_matrix(holes_1d_better) %>% head()
#> V1 V2 V3 V4 V5
#> [1,] 0.34100587 -0.1129065 -0.14519958 0.0357833 0.9211969
#> [2,] 0.47021229 0.2323680 0.49786511 0.3133040 0.6155294
#> [3,] -0.07081708 -0.3776694 -0.10317006 0.3478981 0.8489250
#> [4,] 0.23230438 -0.2897560 0.18500055 0.5184984 0.7476699
#> [5,] -0.10216793 0.4893826 -0.08432954 0.1108271 0.8547937
#> [6,] 0.04188535 -0.1072834 0.37321788 0.7647209 0.5124902
get_best(dplyr::bind_rows(holes_1d_better, holes_1d_geo), group = method)
#> # A tibble: 2 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 <dbl [5 × 1]> 0.914 interpolation search_better NA 5 6 55
#> 2 <dbl [5 × 1]> 0.933 interpolation search_geodesic NA 9 3 152
get_search_count(holes_1d_better)
#> map tries to the x-axis
#> # A tibble: 6 × 2
#> tries n
#> <dbl> <int>
#> 1 1 1
#> 2 2 6
#> 3 3 1
#> 4 4 7
#> 5 5 3
#> 6 6 24
get_search_count(dplyr::bind_rows(holes_1d_better, holes_1d_geo), group = method)
#> map tries to the x-axis
#> # A tibble: 16 × 3
#> # Groups: tries [10]
#> tries method n
#> <dbl> <chr> <int>
#> 1 1 search_better 1
#> 2 2 search_better 6
#> 3 3 search_better 1
#> 4 4 search_better 7
#> 5 5 search_better 3
#> 6 6 search_better 24
#> 7 1 search_geodesic 1
#> 8 2 search_geodesic 11
#> 9 3 search_geodesic 11
#> 10 4 search_geodesic 11
#> 11 5 search_geodesic 11
#> 12 6 search_geodesic 11
#> 13 7 search_geodesic 11
#> 14 8 search_geodesic 11
#> 15 9 search_geodesic 22
#> 16 10 search_geodesic 264
get_interp_last(holes_1d_better)
#> # A tibble: 4 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 <dbl [5 × 1]> 0.753 interpolation search_better NA 2 9 9
#> 2 <dbl [5 × 1]> 0.798 interpolation search_better NA 3 15 24
#> 3 <dbl [5 × 1]> 0.866 interpolation search_better NA 4 7 31
#> 4 <dbl [5 × 1]> 0.914 interpolation search_better NA 5 6 37
get_interp_last(dplyr::bind_rows(holes_1d_better, holes_1d_geo), group = method)
#> # A tibble: 12 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 <dbl [5 × 1]> 0.753 interpolation search_better NA 2 9 9
#> 2 <dbl [5 × 1]> 0.798 interpolation search_better NA 3 15 24
#> 3 <dbl [5 × 1]> 0.866 interpolation search_better NA 4 7 31
#> 4 <dbl [5 × 1]> 0.914 interpolation search_better NA 5 6 37
#> 5 <dbl [5 × 1]> 0.753 interpolation search_geodesic NA 2 7 7
#> 6 <dbl [5 × 1]> 0.793 interpolation search_geodesic NA 3 11 18
#> 7 <dbl [5 × 1]> 0.805 interpolation search_geodesic NA 4 7 25
#> 8 <dbl [5 × 1]> 0.836 interpolation search_geodesic NA 5 8 33
#> 9 <dbl [5 × 1]> 0.890 interpolation search_geodesic NA 6 7 40
#> 10 <dbl [5 × 1]> 0.917 interpolation search_geodesic NA 7 5 45
#> 11 <dbl [5 × 1]> 0.929 interpolation search_geodesic NA 8 4 49
#> 12 <dbl [5 × 1]> 0.933 interpolation search_geodesic NA 9 3 52
res <- holes_1d_geo %>% compute_pca() %>% purrr::pluck("aug")
get_dir_search(res)
#> # A tibble: 330 × 16
#> basis index_val info method alpha tries loop id row_num PC1 PC2
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 <dbl[…]> 0.749 direc… PD NA 2 1 2 2 -1.93 -0.494
#> 2 <dbl[…]> 0.749 direc… PD NA 2 1 3 3 -1.81 -0.515
#> 3 <dbl[…]> 0.749 direc… PD NA 2 1 4 4 -1.78 -0.507
#> 4 <dbl[…]> 0.749 direc… PD NA 2 1 5 5 -1.96 -0.501
#> 5 <dbl[…]> 0.749 direc… PD NA 2 1 6 6 -1.75 -0.555
#> 6 <dbl[…]> 0.749 direc… PD NA 2 1 7 7 -1.99 -0.454
#> 7 <dbl[…]> 0.749 direc… PD NA 2 1 8 8 -1.87 -0.402
#> 8 <dbl[…]> 0.749 direc… PD NA 2 1 9 9 -1.86 -0.606
#> 9 <dbl[…]> 0.749 best_… PD NA 2 1 10 10 -1.98 -0.478
#> 10 <dbl[…]> 0.749 direc… PD NA 2 1 11 11 -1.75 -0.530
#> # ℹ 320 more rows
#> # ℹ 5 more variables: PC3 <dbl>, PC4 <dbl>, PC5 <dbl>, anchor_x <dbl>,
#> # anchor_y <dbl>
best <- matrix(c(0, 1, 0, 0, 0), nrow = 5)
holes_1d_better %>%
bind_theoretical(best, tourr::holes(), raw_data = boa5) %>%
get_theo()
#> # A tibble: 1 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 <dbl [5 × 1]> 0.931 theoretical NA NA NA NA 0