riskyr (version 0.2.0)

plot_icons: Plot an icon array of a population.

Description

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.

Usage

plot_icons(prev = num$prev, sens = num$sens, mirt = NA,
  spec = num$spec, fart = NA, N = freq$N, 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,
  title_lbl = txt$scen_lbl, cex_lbl = 0.9, col_pal = pal,
  transparency = 0.5, mar_notes = TRUE, ...)

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.

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. Accepts values from 1 to 25 (see ?points).

icon_size

Manually specifies the size of the icons via cex (calculated dynamically by default).

icon_brd_lwd

Specifies the border width of icons (if applicable).

block_d

The distance between blocks (does not apply to "filleft", "filltop", and "scatter")

border_d

The distance of icons to the border.

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.

block_size_col

specifies how many icons should be in each block column.

nblocks_row

specifies how many blocks there are in each row. Is calculated by default.

nblocks_col

specifies how many blocks are there in each column. Is calculated by default.

fill_array

specifies how the blocks are filled into the array (Options "left" (default) and "top").

fill_blocks

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

Generic text and color options:

lbl_txt

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

title_lbl

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

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 = TRUE.

...

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_curve, plot_fnet, plot_mosaic, plot_plane, plot_prism, plot_tab, plot_tree

Examples

# NOT RUN {
# ways to work:
plot_icons(N = 1000)  # icon array with default settings (arr_type = "array")
plot_icons(arr_type = "shuffledarray", N = 1000)  # icon array with shuffled IDs

# 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 argument:
plot_icons(N = 1000, by = "all")  # hi, mi, fa, cr (TP, FN, FP, TN) cases
plot_icons(N = 1000, by = "cd")   # (hi + mi) vs. (fa + cr) (TP + FN vs. FP + TN) cases
plot_icons(N = 1000, by = "dc")   # (hi + fa) vs. (mi + cr) (TP + FP vs. FN + TN) cases
plot_icons(N = 1000, by = "ac")   # (hi + cr) vs. (fa + mi) (TP + TN vs. FP + FN) cases

# icon symbols:
plot_icons(N = 100, icon_types = c(21, 23, 24, 23),
               block_size_row = 5, block_size_col = 5, #nblocks_row = 2, nblocks_col = 2,
               block_d = 0.5, border_d = 0.9)

# variants:
plot_icons(N = 800, arr_type = "array", icon_types = c(21, 22, 23, 24),
           block_d = 0.5, border_d = 0.5)

plot_icons(N = 1250, sens = 0.9, spec = 0.9, prev = 0.9,
               icon_types = c(21, 23, 24, 23),
               block_size_row = 10, block_size_col = 5,
               nblocks_row = 5, nblocks_col = 5,
               block_d = 0.8,
               border_d = 0.2,
               fill_array = "top")

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 = "shuffledarray", icon_types = c(21, 23, 24, 22),
           icon_brd_col = grey(.33, .99), icon_brd_lwd = 3, cex_lbl = 1.2)

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",
           title_lbl = "", lbl_txt = txt_TF, col_pal = pal_vir, mar_notes = FALSE)

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

plot_icons(N = 1000, prev = .5, sens = .5, spec = .5, arr_type = "shuffledarray",
           title_lbl = "Shades of blue", col_pal = pal_kn, transparency = .3)

# }