get function

Functions to get components from the data collecting object

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 tourr package
• 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

Returns

a tibble object containing the best basis found by the optimiser(s)

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)

get_anchor(holes_1d_geo)

get_start(holes_1d_better)

get_interrupt(holes_1d_better)

get_interp(holes_1d_better) %>% head()

get_basis_matrix(holes_1d_better) %>% head()

get_best(dplyr::bind_rows(holes_1d_better, holes_1d_geo), group = method)

get_search_count(holes_1d_better)
get_search_count(dplyr::bind_rows(holes_1d_better, holes_1d_geo), group = method)

get_interp_last(holes_1d_better)
get_interp_last(dplyr::bind_rows(holes_1d_better, holes_1d_geo), group = method)

res <- holes_1d_geo %>% compute_pca() %>% purrr::pluck("aug")
get_dir_search(res)

best <- matrix(c(0, 1, 0, 0, 0), nrow = 5)
holes_1d_better %>%
  bind_theoretical(best, tourr::holes(), raw_data = boa5) %>%
  get_theo()