Plot an icon array of a population.

Source: R/plot_icons.R

plot_icons plots a population of which individual's condition has been classified correctly or incorrectly as icons from a sufficient and valid set of 3 essential probabilities (prev, and sens or its complement mirt, and spec or its complement fart) or existing frequency information freq and a population size of N individuals.

plot_icons(
  prev = num$prev,
  sens = num$sens,
  mirt = NA,
  spec = num$spec,
  fart = NA,
  N = freq$N,
  sample = FALSE,
  arr_type = "array",
  by = "all",
  ident_order = c("hi", "mi", "fa", "cr"),
  icon_types = 22,
  icon_size = NULL,
  icon_brd_lwd = 1.5,
  block_d = NULL,
  border_d = 0.1,
  block_size_row = 10,
  block_size_col = 10,
  nblocks_row = NULL,
  nblocks_col = NULL,
  fill_array = "left",
  fill_blocks = "rowwise",
  lbl_txt = txt,
  main = txt$scen_lbl,
  sub = "type",
  title_lbl = NULL,
  cex_lbl = 0.9,
  col_pal = pal,
  transparency = 0.5,
  mar_notes = FALSE,
  ...
)

Arguments

prev

The condition's prevalence prev (i.e., the probability of condition being TRUE).

sens

The decision's sensitivity sens (i.e., the conditional probability of a positive decision provided that the condition is TRUE). sens is optional when its complement mirt is provided.

mirt

The decision's miss rate mirt (i.e., the conditional probability of a negative decision provided that the condition is TRUE). mirt is optional when its complement sens is provided.

spec

The decision's specificity value spec (i.e., the conditional probability of a negative decision provided that the condition is FALSE). spec is optional when its complement fart is provided.

fart

The decision's false alarm rate fart (i.e., the conditional probability of a positive decision provided that the condition is FALSE). fart is optional when its complement spec is provided.

N

The number of individuals in the population. A suitable value of N is computed, if not provided. If N is 100,000 or greater it is reduced to 10,000 for the array types if the frequencies allow it.

sample

Boolean value that determines whether frequency values are sampled from N, given the probability values of prev, sens, and spec. Default: sample = FALSE.

arr_type

The icons can be arranged in different ways resulting in different types of displays:

  1. arr_type = "array": Icons are plotted in a classical icon array (default). Icons can be arranged in blocks using block_d. The order of filling the array can be customized using fill_array and fill_blocks.

  2. arr_type = "shuffledarray": Icons are plotted in an icon array, but positions are shuffled (randomized). Icons can be arranged in blocks using block_d. The order of filling the array can be customized using fill_array and fill_blocks.

  3. arr_type = "mosaic": Icons are ordered like in a mosaic plot. The area size displays the relative proportions of their frequencies.

  4. arr_type = "fillequal": Icons are positioned into equally sized blocks. Thus, their density reflects the relative proportions of their frequencies.

  5. arr_type = "fillleft": Icons are randomly filled from the left.

  6. arr_type = "filltop": Icons are randomly filled from the top.

  7. arr_type = "scatter": Icons are randomly scattered into the plot.

by

A character code specifying a perspective to split the population into subsets, with 4 options:

  1. "all": by condition (cd) and by decision (dc):

    hi, mi, fa, cr cases (default);

  2. "cd": by condition (cd) only:

    cond_true vs. cond_false cases;

  3. "dc": by decision (dc) only:

    dec_pos vs. dec_neg cases;

  4. "ac": by accuracy (ac) only:

    dec_cor vs. dec_err cases.

ident_order

The order in which icon identities (i.e., hi, mi, fa, and cr) are plotted. Default: ident_order = c("hi", "mi", "fa", "cr")

icon_types

specifies the appearance of the icons as a vector. Default: icon_types = 11 (i.e., squares with border). Accepts values from 1 to 25 (see ?points).

icon_size

specifies the size of the icons via cex Default: icon_size = NULL for automatic calculation.

icon_brd_lwd

specifies the border width of icons (if applicable). Default: icon_brd_lwd = 1.5. Set to NA for no border.

block_d

The distance between blocks. Default: block_d = NULL for automatic calculation; (does not apply to "filleft", "filltop", and "scatter")

border_d

The distance of icons to the border. Default: border_d = 0.1.

Additional options for controlling the arrangement of arrays (for arr_type = "array" and "shuffledarray"):

block_size_row

specifies how many icons should be in each block row. Default: block_size_row = 10.

block_size_col

specifies how many icons should be in each block column. Default: block_size_col = 10.

nblocks_row

Number of blocks per row. Default: nblocks_row = NULL for automatic calculation.

nblocks_col

Number of blocks per column. Default: nblocks_col = NULL for automatic calculation.

fill_array

specifies how the blocks are filled into the array. Options: fill_array = "left" (default) vs. "top".

fill_blocks

specifies how icons within blocks are filled. Options: fill_blocks = "rowwise" (default) and "colwise".

Generic text and color options:

lbl_txt

Default label set for text elements. Default: lbl_txt = txt.

main

Text label for main plot title. Default: main = txt$scen_lbl.

sub

Text label for plot subtitle (on 2nd line). Default: sub = "type" shows information on current plot type.

title_lbl

Deprecated text label for current plot title. Replaced by main.

cex_lbl

Scaling factor for text labels. Default: cex_lbl = .90.

col_pal

Color palette. Default: col_pal = pal.

transparency

Specifies the transparency for overlapping icons (not for arr_type = "array" and "shuffledarray").

mar_notes

Boolean option for showing margin notes. Default: mar_notes = FALSE.

...

Other (graphical) parameters.

Value

Nothing (NULL).

Details

If probabilities are provided, a new list of natural frequencies freq is computed by comp_freq. By contrast, if no probabilities are provided, the values currently contained in freq are used. By default, comp_freq rounds frequencies to nearest integers to avoid decimal values in freq.

See also

Other visualization functions: plot.riskyr(), plot_area(), plot_bar(), plot_crisk(), plot_curve(), plot_fnet(), plot_mosaic(), plot_plane(), plot_prism(), plot_tab(), plot_tree()

Examples

# Basics:
plot_icons(N = 1000)  # icon array with default settings (arr_type = "array")

plot_icons(arr_type = "shuffledarray", N = 1000)  # icon array with shuffled IDs


# Sampling:
plot_icons(N = 1000, prev = 1/2, sens = 2/3, spec = 6/7, sample = TRUE)


# array types:
plot_icons(arr_type = "mosaic",    N = 1000)  # areas as in mosaic plot

plot_icons(arr_type = "fillequal", N = 1000)  # areas of equal size (probability as density)

plot_icons(arr_type = "fillleft",  N = 1000)  # icons filled from left to right (in columns)

plot_icons(arr_type = "filltop",   N = 1000)  # icons filled from top to bottom (in rows)

plot_icons(arr_type = "scatter",   N = 1000)  # icons randomly scattered


# by:
plot_icons(N = 1000, by = "all")  # hi, mi, fa, cr (TP, FN, FP, TN) cases

plot_icons(N = 1000, by = "cd", main = "Cases by condition")  # (hi + mi) vs. (fa + cr)

plot_icons(N = 1000, by = "dc", main = "Cases by decision")   # (hi + fa) vs. (mi + cr)

plot_icons(N = 1000, by = "ac", main = "Cases by accuracy")   # (hi + cr) vs. (fa + mi)


# Custom icon types and colors:
plot_icons(N = 800, arr_type = "array", icon_types = c(21, 22, 23, 24),
           block_d = 0.5, border_d = 0.5, col_pal = pal_vir)


plot_icons(N = 800, arr_type = "shuffledarray", icon_types = c(21, 23, 24, 22),
           block_d = 0.5, border_d = 0.5)


plot_icons(N = 800, arr_type = "fillequal", icon_types = c(21, 22, 22, 21),
           icon_brd_lwd = .5, cex = 1, cex_lbl = 1.1)


# Text and color options:
plot_icons(N = 1000, prev = .5, sens = .5, spec = .5, arr_type = "shuffledarray",
           main = "My title", sub = NA, lbl_txt = txt_TF, col_pal = pal_vir, mar_notes = TRUE)


plot_icons(N = 1000, prev = .5, sens = .5, spec = .5, arr_type = "shuffledarray",
           main = "Green vs. red", col_pal = pal_rgb, transparency = .5)