The ferrn package extracts key components in the data object collected by the guided tour optimisation, and produces diagnostic plots. An associated paper can be found at https://arxiv.org/abs/2104.03448.

Installation

You can install the development version of ferrn from GitHub with:

# install.packages("remotes")
remotes::install_github("huizezhang-sherry/ferrn")

Usage

The best projection basis found by the projection pursuit algorithm can be extracted via

library(ferrn)
library(dplyr)
holes_1d_better %>% get_best()
#> # A tibble: 1 x 8
#>   basis             index_val info          method       alpha tries  loop    id
#>   <list>                <dbl> <chr>         <chr>        <dbl> <dbl> <dbl> <int>
#> 1 <dbl[,1] [5 × 1]>     0.914 interpolation search_bett…    NA     5     6    55
holes_1d_better %>% get_best() %>% pull(basis) %>% .[[1]]
#>              [,1]
#> [1,]  0.005468276
#> [2,]  0.990167039
#> [3,] -0.054198426
#> [4,]  0.088415793
#> [5,]  0.093725721
holes_1d_better %>% get_best() %>% pull(index_val)
#> [1] 0.9136095

Trace plot for viewing the optimisation progression with botanical palette:

Compare two algorithms via plotting the projection bases on the reduced PCA space:

bind_rows(holes_1d_geo, holes_1d_better) %>%
  bind_theoretical(matrix(c(0, 1, 0, 0, 0), nrow = 5),
                   index = tourr::holes(), raw_data = boa5) %>% 
  explore_space_pca(group = method, details = TRUE)  +
  scale_color_discrete_botanical()

View the projection bases on its original 5-D space via tour animation:

bind_rows(holes_1d_geo, holes_1d_better) %>%
  explore_space_tour(flip = TRUE, group = method,
                     palette = botanical_palettes$fern[c(1, 6)],
                     max_frames = 20, 
                     point_size = 2, end_size = 5)