ferrn

The ferrn package extracts key components in the data object collected by the guided tour optimisation, and produces diagnostic plots.

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:

holes_1d_better %>% 
  explore_trace_interp() + 
  scale_color_continuous_botanical()

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)