Title: Facilitate Exploration of touRR optimisatioN
Version: 0.1.0
Description: Diagnostic plots for optimisation, with a focus on projection pursuit. These show paths the optimiser takes in the high-dimensional space in multiple ways: by reducing the dimension using principal component analysis, and also using the tour to show the path on the high-dimensional space. Several botanical colour palettes are included, reflecting the name of the package. A paper describing the methodology can be found at https://journal.r-project.org/archive/2021/RJ-2021-105/index.html.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
URL: https://github.com/huizezhang-sherry/ferrn/
BugReports: https://github.com/huizezhang-sherry/ferrn/issues
Imports: rlang (≥ 0.1.2), dplyr, magrittr, scales, gganimate, ggplot2, tibble, purrr, tourr, stringr, ggrepel, ggforce, tidyr, cli, progress, glue, GpGp,
RoxygenNote: 7.3.1
Depends: R (≥ 2.10)
Suggests: roxygen2, covr, pkgdown, testthat, forcats, patchwork, future.apply,
Language: en-GB
NeedsCompilation: no
Packaged: 2024-06-23 18:20:02 UTC; hzha400
Author: H. Sherry Zhang ORCID iD [aut, cre], Dianne Cook ORCID iD [aut], Ursula Laa ORCID iD [aut], Nicolas Langrené ORCID iD [aut], Patricia Menéndez ORCID iD [aut]
Maintainer: H. Sherry Zhang <huizezhangsh@gmail.com>
Repository: CRAN
Date/Publication: 2024-06-23 22:30:07 UTC

ferrn: Facilitate Exploration of touRR optimisatioN

Description

logo

Diagnostic plots for optimisation, with a focus on projection pursuit. These show paths the optimiser takes in the high-dimensional space in multiple ways: by reducing the dimension using principal component analysis, and also using the tour to show the path on the high-dimensional space. Several botanical colour palettes are included, reflecting the name of the package. A paper describing the methodology can be found at https://journal.r-project.org/archive/2021/RJ-2021-105/index.html.

Author(s)

Maintainer: H. Sherry Zhang huizezhangsh@gmail.com (ORCID)

Authors:

See Also

Useful links:

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Value

this is a magrittr pipe!

A ggproto for drawing anchor points

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_anchor(dt, anchor_size = 3, anchor_alpha = 0.5, anchor_color = NULL, ...)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

anchor_size

numeric; the size of the anchor points

anchor_alpha

numeric; the alpha of the anchor points

anchor_color

the variable to be coloured by

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for drawing anchor points in explore_space_pca()

See Also

Other draw functions: add_anno(), add_dir_search(), add_end(), add_interp(), add_interp_last(), add_interrupt(), add_search(), add_space(), add_start(), add_theo()

A ggproto for annotating the symmetry of the starting points

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_anno(dt, anno_color = "black", anno_lty = "dashed", anno_alpha = 0.1, ...)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

anno_color

character; the colour of the annotation line

anno_lty

character; the linetype of the annotation line

anno_alpha

numeric; the alpha of the annotation line

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for annotating the symmetry of start points in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_dir_search(), add_end(), add_interp(), add_interp_last(), add_interrupt(), add_search(), add_space(), add_start(), add_theo()

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_dir_search(dt, dir_size = 0.5, dir_alpha = 0.5, dir_color = NULL, ...)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

dir_size

numeric; the size of the directional search points in pseudo derivative search

dir_alpha

numeric; the alpha of the directional search points in pseudo derivative search

dir_color

the variable to be coloured by

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for drawing directional search points (used in pseudo derivative search) with buffer in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_anno(), add_end(), add_interp(), add_interp_last(), add_interrupt(), add_search(), add_space(), add_start(), add_theo()

A ggproto for drawing start points

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_end(dt, end_size = 5, end_alpha = 1, end_color = NULL, ...)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

end_size

numeric; the size of the end point

end_alpha

numeric; the alpha of the end point

end_color

the variable to be coloured by

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for drawing end points in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_anno(), add_dir_search(), add_interp(), add_interp_last(), add_interrupt(), add_search(), add_space(), add_start(), add_theo()

A ggproto for drawing interpolation path

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_interp(
  dt,
  interp_size = 1.5,
  interp_alpha = NULL,
  interp_color = NULL,
  interp_group = NULL,
  ...
)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

interp_size

numeric; the size of the interpolation path

interp_alpha

numeric; the alpha of the interpolation path

interp_color

the variable to be coloured by

interp_group

the variable to label different interpolation path

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for drawing the interpolation points in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_anno(), add_dir_search(), add_end(), add_interp_last(), add_interrupt(), add_search(), add_space(), add_start(), add_theo()

A ggproto for drawing finish points

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_interp_last(
  dt,
  interp_last_size = 3,
  interp_last_alpha = 1,
  interp_last_color = NULL,
  ...
)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

interp_last_size

numeric; the size of the last interpolation points in each iteration

interp_last_alpha

numeric; the alpha of the last interpolation points in each iteration

interp_last_color

the variable to be coloured by

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for drawing the last interpolation points of each iteration in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_anno(), add_dir_search(), add_end(), add_interp(), add_interrupt(), add_search(), add_space(), add_start(), add_theo()

A ggproto for annotating the interrupted path

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_interrupt(
  dt,
  interrupt_size = 0.5,
  interrupt_alpha = NULL,
  interrupt_color = NULL,
  interrupt_group = NULL,
  interrupt_linetype = "dashed",
  ...
)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

interrupt_size

numeric; the size of the interruption path

interrupt_alpha

numeric; the alpha of the interruption path

interrupt_color

the variable to be coloured by

interrupt_group

the variable to label different interruption

interrupt_linetype

character; the linetype to annotate the interruption

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for annotating the interruption in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_anno(), add_dir_search(), add_end(), add_interp(), add_interp_last(), add_search(), add_space(), add_start(), add_theo()

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_search(dt, search_size = 0.5, search_alpha = 0.5, search_color = NULL, ...)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

search_size

numeric; the size of the search points

search_alpha

numeric; the alpha of the anchor points

search_color

the variable to be coloured by

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for drawing search points in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_anno(), add_dir_search(), add_end(), add_interp(), add_interp_last(), add_interrupt(), add_space(), add_start(), add_theo()

A ggproto for drawing circle

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_space(
  dt,
  space_alpha = 0.5,
  space_fill = "grey92",
  space_color = "white",
  cent_size = 1,
  cent_alpha = 1,
  cent_color = "black",
  ...
)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

space_alpha

numeric; the alpha of the basis space

space_fill

character; the colour of the space filling

space_color

character; the colour of the space brim

cent_size

numeric; the size of the centre point

cent_alpha

numeric; an alpha of the centre point

cent_color

character; the colour of the centre point

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for drawing the space in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_anno(), add_dir_search(), add_end(), add_interp(), add_interp_last(), add_interrupt(), add_search(), add_start(), add_theo()

Examples

library(ggplot2)
space <- tibble::tibble(x0 = 0, y0 = 0, r = 5)
ggplot() +
  add_space(space) +
  theme_void() +
  theme(aspect.ratio = 1)

A ggproto for drawing start points

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_start(dt, start_size = 5, start_alpha = 1, start_color = NULL, ...)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

start_size

numeric; the size of start point

start_alpha

numeric; the alpha of start point

start_color

the variable to be coloured by

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for drawing start points in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_anno(), add_dir_search(), add_end(), add_interp(), add_interp_last(), add_interrupt(), add_search(), add_space(), add_theo()

Examples

library(ggplot2)
# construct the space and start df for plotting
space <- tibble::tibble(x0 = 0, y0 = 0, r = 5)
holes_1d_geo %>%
  compute_pca() %>%
  purrr::pluck("aug") %>%
  clean_method() %>%
  get_start()

A ggproto for drawing the theoretical basis, if applicable

Description

This is a wrapper function used by explore_space_pca() and should be be called directly by the user

Usage

add_theo(
  dt,
  theo_label = "*",
  theo_size = 25,
  theo_alpha = 0.8,
  theo_color = "#000000",
  ...
)

Arguments

dt

A data object from the running the optimisation algorithm in guided tour

theo_label

character; a symbol to label the theoretical point

theo_size

numeric; the size of the theoretical point

theo_alpha

numeric; the alpha of the theoretical point

theo_color

character; the colour of the theoretical point in hex

...

other aesthetics inherent from explore_space_pca()

Value

a wrapper for drawing theoretical points in explore_space_pca()

See Also

Other draw functions: add_anchor(), add_anno(), add_dir_search(), add_end(), add_interp(), add_interp_last(), add_interrupt(), add_search(), add_space(), add_start()

Bind random bases in the projection bases space

Description

Given the orthonormality constraint, the projection bases live in a high dimensional hollow sphere. Generating random points on the sphere is useful to perceive the data object in the high dimensional space.

Usage

bind_random(dt, n = 500, seed = 1)

Arguments

dt

a data object collected by the projection pursuit guided tour optimisation in the tourr package

n

numeric; the number of random bases to generate in each dimension by geozoo

seed

numeric; a seed for generating reproducible random bases from geozoo

Value

a tibble object containing both the searched and random bases

See Also

Other bind: bind_random_matrix(), bind_theoretical()

Examples

bind_random(holes_1d_better) %>% tail(5)

Bind random bases in the projection bases space as a matrix

Description

Bind random bases in the projection bases space as a matrix

Usage

bind_random_matrix(basis, n = 500, d = 1, front = FALSE, seed = 1)

Arguments

basis

a matrix returned by get_basis_matrix()

n

numeric; the number of random bases to generate in each dimension by geozoo

d

numeric; dimension of the basis, d = 1, 2, ...

front

logical; if the random bases should be bound before or after the original bases

seed

numeric; a seed for generating reproducible random bases from geozoo

Value

matrix

a matrix containing both the searched and random bases

See Also

Other bind: bind_random(), bind_theoretical()

Examples

data <- get_basis_matrix(holes_1d_geo)
bind_random_matrix(data) %>% tail(5)

Bind the theoretical best record

Description

The theoretical best basis is usually known for a simulated problem. Augment this information into the data object allows for evaluating the performance of optimisation against the theory.

Usage

bind_theoretical(dt, matrix, index, raw_data)

Arguments

dt

a data object collected by the projection pursuit guided tour optimisation in the tourr package

matrix

a matrix of the theoretical basis

index

the index function used to calculate the index value

raw_data

a tibble of the original data used to calculate the index value

Value

a tibble object containing both the searched and theoretical best bases

See Also

Other bind: bind_random(), bind_random_matrix()

Examples

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

A customised colour palette based on Australian botanies

Description

Available colours in the palettes

Usage

botanical_palettes

botanical_pal(palette = "fern", reverse = FALSE)

Arguments

palette

Colour palette from the botanical_palette

reverse

logical, if the colour should be reversed

Format

An object of class list of length 5.

Value

a function for interpolating colour in the botanical palette

Clean method names

Description

Clean method names

Usage

clean_method(dt)

Arguments

dt

a data object

Value

a tibble with method cleaned

Examples

head(clean_method(holes_1d_better), 5)

Plot the PCA projection of the projection bases space

Description

Plot the PCA projection of the projection bases space

Usage

explore_space_start(dt, group = NULL, pca = TRUE, ...)

explore_space_end(dt, group = NULL, pca = TRUE, ...)

explore_space_pca(
  dt,
  details = FALSE,
  pca = TRUE,
  group = NULL,
  color = NULL,
  facet = NULL,
  ...,
  animate = FALSE
)

Arguments

dt

a data object collected by the projection pursuit guided tour optimisation in tourr

group

the variable to label different runs of the optimiser(s)

pca

logical; if PCA coordinates need to be computed for the data

...

other arguments passed to add_*() functions

details

logical; if components other than start, end and interpolation need to be shown

color

the variable to be coloured by

facet

the variable to be faceted by

animate

logical; if the interpolation path needs to be animated

Value

a ggplot2 object

See Also

Other main plot functions: explore_space_tour(), explore_trace_interp(), explore_trace_search()

Examples

dplyr::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()

## Not run: 
best <- matrix(c(0, 1, 0, 0, 0), nrow = 5)
dt <- bind_theoretical(holes_1d_jellyfish, best, tourr::holes(), raw_data = boa5)
explore_space_start(dt)
explore_space_end(dt, group = loop, theo_size = 10, theo_color = "#FF0000")
explore_space_pca(
  dt, facet = loop, interp_size = 0.5, theo_size = 10,
  start_size = 1, end_size = 3
  )

## End(Not run)

Plot the grand tour animation of the bases space in high dimension

Description

Plot the grand tour animation of the bases space in high dimension

Usage

explore_space_tour(..., axes = "bottomleft")

prep_space_tour(
  dt,
  group = NULL,
  flip = FALSE,
  n_random = 2000,
  color = NULL,
  rand_size = 1,
  rand_color = "#D3D3D3",
  point_size = 1.5,
  end_size = 5,
  theo_size = 3,
  theo_shape = 17,
  theo_color = "black",
  palette = botanical_palettes$fern,
  ...
)

Arguments

...

other argument passed to tourr::animate_xy() and prep_space_tour()

axes

see [tourr::animate_xy()]

dt

a data object collected by the projection pursuit guided tour optimisation in tourr

group

the variable to label different runs of the optimiser(s)

flip

logical; if the sign flipping need to be performed

n_random

numeric; the number of random basis to generate

color

the variable to be coloured by

rand_size

numeric; the size of random points

rand_color

character; the color hex code for random points

point_size

numeric; the size of points searched by the optimiser(s)

end_size

numeric; the size of end points

theo_size

numeric; the size of theoretical point(s)

theo_shape

numeric; the shape symbol in the basic plot

theo_color

character; the color of theoretical point(s)

palette

the colour palette to be used

Value

explore_space_tour()

an animation of the search path in the high-dimensional sphere

prep_space_tour()

a list containing various components needed for producing the animation

See Also

Other main plot functions: explore_space_start(), explore_trace_interp(), explore_trace_search()

Examples

explore_space_tour(dplyr::bind_rows(holes_1d_better, holes_1d_geo),
  group = method, palette = botanical_palettes$fern[c(1, 6)]
)

Plot the trace the search progression

Description

Trace the index value of search/ interpolation points in guided tour optimisation

Usage

explore_trace_interp(
  dt,
  iter = NULL,
  color = NULL,
  group = NULL,
  cutoff = 50,
  target_size = 3,
  interp_size = 1,
  accuracy_x = 5,
  accuracy_y = 0.01
)

Arguments

dt

a data object collected by the projection pursuit guided tour optimisation in tourr

iter

the variable to be plotted on the x-axis

color

the variable to be coloured by

group

the variable to label different runs of the optimiser(s)

cutoff

numeric; if the number of interpolating points is smaller than cutoff, all the interpolation points will be plotted as dots

target_size

numeric; the size of target points in the interpolation

interp_size

numeric; the size of interpolation points

accuracy_x

numeric; If the difference of two neighbour x-labels is smaller than accuracy_x, only one of them will be displayed. Used for better axis label

accuracy_y

numeric; the precision of y-axis label

Value

a ggplot object for diagnosing how the index value progresses during the interpolation

See Also

Other main plot functions: explore_space_start(), explore_space_tour(), explore_trace_search()

Examples

# Compare the trace of interpolated points in two algorithms
holes_1d_better %>%
  explore_trace_interp(interp_size = 2) +
  scale_color_continuous_botanical(palette = "fern")

Description

Plot the count in each iteration

Usage

explore_trace_search(
  dt,
  iter = NULL,
  color = NULL,
  cutoff = 15,
  extend_lower = 0.95,
  ...
)

Arguments

dt

a data object collected by the projection pursuit guided tour optimisation in tourr

iter

the variable to be plotted on the x-axis

color

the variable to be coloured by

cutoff

numeric; if the number of searches in one iteration is smaller than cutoff, a point geom, rather than boxplot geom, will be used.

extend_lower

a numeric for extending the y-axis to display text labels

...

arguments passed into geom_label_repel() for displaying text labels

Value

a ggplot object for diagnosing how many points the optimiser(s) have searched

See Also

Other main plot functions: explore_space_start(), explore_space_tour(), explore_trace_interp()

Examples

# Summary plots for search points in two algorithms
library(patchwork)
library(dplyr)
library(ggplot2)
p1 <- holes_1d_better %>% explore_trace_search() +
  scale_color_continuous_botanical(palette = "fern")
p2 <- holes_2d_better_max_tries %>% explore_trace_search() +
  scale_color_continuous_botanical(palette = "daisy")
p1 / p2

Helper functions for 'explore_space_pca()'

Description

Helper functions for 'explore_space_pca()'

Usage

flip_sign(dt, group = NULL, ...)

compute_pca(dt, group = NULL, random = TRUE, flip = TRUE, ...)

Arguments

dt

a data object collected by the projection pursuit guided tour optimisation in tourr

group

the variable to label different runs of the optimiser(s)

...

other arguments received from explore_space_pca()

random

logical; if random bases from the basis space need to be added to the data

flip

logical; if the sign flipping need to be performed

Value

flip_sign(): a list containing a matrix of all the bases, a logical value indicating whether a flip of sign is performed, and a data frame of the original dataset.

compute_pca(): a list containing the PCA summary and a data frame with PC coordinates augmented.

Examples

dt <- dplyr::bind_rows(holes_1d_geo, holes_1d_better)
 flip_sign(dt, group = method) %>% str(max = 1)
compute_pca(dt, group = method)

Better label formatting to avoid overlapping

Description

Better label formatting to avoid overlapping

Usage

format_label(labels, accuracy)

Arguments

labels

a numerical vector of labels

accuracy

the accuracy of the label

Value

a vector of adjusted labels

Examples

format_label(c(0.87, 0.87, 0.9, 0.93, 0.95), 0.01)
format_label(c(0.87, 0.87, 0.9, 0.93, 0.95, 0.96, 0.96), 0.01)

Functions to get components from the data collecting object

Description

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 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

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

Value

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

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()

Data objects collected during the projection pursuit optimisation

Description

Simulated data to demonstrate the usage of four diagnostic plots in the package, users can create their own guided tour data objects and diagnose with the visualisation designed in this package.

Usage

holes_1d_geo

holes_1d_better

holes_1d_jellyfish

holes_2d_better

holes_2d_better_max_tries

Format

An object of class tbl_df (inherits from tbl, data.frame) with 416 rows and 8 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 79 rows and 8 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 2500 rows and 8 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 98 rows and 8 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1499 rows and 8 columns.

Details

The prefix holes_* indicates the use of holes index in the guided tour. The suffix *_better/geo/jellyfish indicates the optimiser used: search_better, search_geodesic, search_jellyfish.

Examples

holes_1d_better %>%
explore_trace_interp(interp_size = 2) +
  scale_color_continuous_botanical(palette = "fern")

Plot the projection from the optimisation data collected from projection pursuit

Description

Plot the projection from the optimisation data collected from projection pursuit

Usage

plot_projection(dt, data, cols = NULL)

compute_projection(dt, data, cols = NULL)

Arguments

dt

a data object collected by the projection pursuit guided tour optimisation in tourr

data

the original data

cols

additional columns to include in the plot

Value

a ggplot object

Examples

holes_1d_jellyfish |> get_best() |> plot_projection(data = boa5)

Function to calculate smoothness and squintability

Description

Function to calculate smoothness and squintability

Usage

sample_bases(
  idx,
  data = sine1000,
  n_basis = 300,
  parallel = FALSE,
  best = matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1), nrow = 6),
  min_proj_dist = NA,
  step_size = NA,
  seed = 123
)

## S3 method for class 'basis_df'
print(x, width = NULL, ...)

## S3 method for class 'basis_df'
tbl_sum(x)

calc_smoothness(
  basis_df,
  start_params = c(0.001, 0.5, 2, 2),
  other_gp_params = NULL,
  verbose = FALSE
)

## S3 method for class 'smoothness_res'
print(x, width = NULL, ...)

## S3 method for class 'smoothness_res'
tbl_sum(x)

calc_squintability(
  basis_df,
  method = c("ks", "nls"),
  scale = TRUE,
  bin_width = 0.005,
  other_params = NULL
)

## S3 method for class 'squintability_res'
print(x, width = NULL, ...)

## S3 method for class 'squintability_res'
tbl_sum(x)

fit_ks(basis_df, idx, other_params = NULL)

fit_nls(basis_df, other_params = NULL)

Arguments

idx

character, the name of projection pursuit index function, e.g. "holes"

data

a matrix or data frame, the high dimensional data to be projected

n_basis

numeric, the number of random bases to generate

parallel

logic, whether to use parallel computing for calculating the index. Recommend for the stringy index.

best

a matrix, the theoretical/ empirical best projection matrix to calculate the projection distance from the simulated random bases.

min_proj_dist

only for squintability, the threshold for projection distance for the random basis to be considered in sampling

step_size

numeric, step size for interpolating from each random basis to the best basis, recommend 0.005

seed

numeric, seed for sampling random bases

x

objects with specialised printing methods

width

only used when max.levels is NULL, see above.

...

further arguments passed to or from other methods.

basis_df

the basis data frame returned from sample_bases

start_params

list, the starting parameters for the Gaussian process for smoothness

other_gp_params

list, additional parameters to be passed to [GpGp::fit_model()] for calculating smoothness

verbose

logical, whether to print optimisation progression when fitting the Gaussian process

method

either "ks" (kernel smoothing) or "nls" (non-linear least square) for calculating squintability.

scale

logic, whether to scale the index value to 0-1 in squintability

bin_width

numeric, the bin width to average the index value before fitting the kernel, recommend to set as the same as 'step' parameter

other_params

list additional parameters for fitting kernel smoothing or non-linear least square, see [stats::ksmooth()] and [stats::nls()] for details

Examples

## Not run: 
library(GpGp)
library(fields)
library(tourr)
basis_smoothness <- sample_bases(idx = "holes")
calc_smoothness(basis_smoothness)
basis_squint <- sample_bases(idx = "holes", n_basis = 100, step_size = 0.01, min_proj_dist = 1.5)
calc_squintability(basis_squint, method = "ks", bin_width = 0.01)

## End(Not run)

continuous scale colour function

Description

continuous scale colour function

Discrete scale colour function

continuous scale fill function

discrete scale fill function

Usage

scale_color_continuous_botanical(palette = "fern", reverse = FALSE, ...)

scale_color_discrete_botanical(palette = "fern", reverse = FALSE, ...)

scale_fill_continuous_botanical(palette = "fern", reverse = FALSE, ...)

scale_fill_discrete_botanical(palette = "fern", reverse = FALSE, ...)

Arguments

palette

colour palette from the botanical_palette

reverse

logical; if the colour should be reversed

...

other arguments passed into scale_color_gradientn

Value

a wrapper for continuous scales in the botanical palette

a wrapper for discrete scales in the botanical palette

a wrapper for continuous fill in the botanical palette

a wrapper for discrete fill in the botanical palette

Simulated sine, pipe, and gaussian mixture

Description

Simulated sine and pipe data for calculating optimisation features. Each dataset has 1000 observations and the last two columns contain the intended structure with the rest being noise.

Usage

sine1000

sine1000_8d

pipe1000

pipe1000_8d

pipe1000_10d

pipe1000_12d

boa

boa5

boa6

Format

An object of class matrix (inherits from array) with 1000 rows and 6 columns.

An object of class matrix (inherits from array) with 1000 rows and 8 columns.

An object of class matrix (inherits from array) with 1000 rows and 6 columns.

An object of class matrix (inherits from array) with 1000 rows and 8 columns.

An object of class matrix (inherits from array) with 1000 rows and 10 columns.

An object of class matrix (inherits from array) with 1000 rows and 12 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1000 rows and 10 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1000 rows and 5 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1000 rows and 6 columns.

Examples

library(ggplot2)
library(tidyr)
library(dplyr)
boa %>%
  pivot_longer(cols = x1:x10, names_to = "var", values_to = "value") %>%
  mutate(var = forcats::fct_relevel(as.factor(var), paste0("x", 1:10))) %>%
  ggplot(aes(x = value)) +
  geom_density() +
  facet_wrap(vars(var))

sine1000 |> ggplot(aes(x = V5, y = V6)) + geom_point() + theme(aspect.ratio = 1)
pipe1000_8d |> ggplot(aes(x = V5, y = V6)) + geom_point() + theme(aspect.ratio = 1)
pipe1000_8d |> ggplot(aes(x = V7, y = V8)) + geom_point() + theme(aspect.ratio = 1)

A specific theme for trace plots

Description

A specific theme for trace plots

Usage

theme_fern()

Value

a ggplot2 theme for explore_trace_interp()