Plot a cumulative risk curve.

Source: R/plot_crisk.R

plot_crisk creates visualizations of cumulative risks.

plot_crisk(
  x,
  y = NULL,
  x_from = NA,
  x_to = NA,
  fit_curve = FALSE,
  show_pas = FALSE,
  show_rem = FALSE,
  show_pop = FALSE,
  show_aux = FALSE,
  show_num = FALSE,
  show_inc = FALSE,
  show_grid = FALSE,
  col_pal = pal_crisk,
  arr_c = -3,
  main = txt$scen_lbl,
  sub = "type",
  title_lbl = NULL,
  x_lbl = "Age (in years)",
  y_lbl = "Population risk",
  y2_lbl = "",
  mar_notes = FALSE,
  ...
)

Arguments

x

Data or values of an x-dimension on which risk is expressed (required). If x but not y is provided, xy.coords from grDevices is used to determine x- and y-values.

y

Values of cumulative risks on a y-dimension (optional, if x is an appropriate structure), as monotonically increasing percentage values (ranging from 0 to 100). Default: y = NULL.

x_from

Start value of risk increment. Default: x_from = NA.

x_to

End value of risk increment. Default: x_to = NA.

fit_curve

Boolean: Fit a curve to x-y-data? Default: fit_curve = FALSE.

show_pas

Boolean: Show past/passed risk? Default: show_pas = FALSE.

show_rem

Boolean: Show remaining risk? Default: show_rem = FALSE.

show_pop

Boolean: Show population partitions? Default: show_pop = FALSE.

show_aux

Boolean: Show auxiliary elements (i.e., explanatory lines, points, and labels)? Default: show_aux = FALSE.

show_num

Boolean: Show numeric values, provided that show_aux = TRUE. Default: show_num = FALSE.

show_inc

Boolean: Show risk increments? Default: show_inc = FALSE.

show_grid

Boolean: Show grid lines? Default: show_grid = FALSE.

col_pal

Color palette (as a named vector). Default: col_pal = pal_crisk.

arr_c

Arrow code for symbols at ends of population links (as a numeric value -3 <= arr_c <= +6), with the following options:

  • -1 to -3: points at one/other/both end/s;

  • 0: no symbols;

  • +1 to +3: V-arrow at one/other/both end/s;

  • +4 to +6: T-arrow at one/other/both end/s.

Default: arr_c = -3 (points at both ends).

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.

x_lbl

Text label of x-axis (at bottom). Default: x_lbl = "Age (in years)".

y_lbl

Text label of y-axis (on left). Default: y_lbl = "Population risk".

y2_lbl

Text label of 2nd y-axis (on right). Default: y2_lbl = "" (formerly "Remaining risk").

mar_notes

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

...

Other (graphical) parameters.

Value

Nothing (NULL).

Details

plot_crisk assumes data inputs x and y that correspond to each other so that y is a (monotonically increasing) probability density function (over cumulative risk amounts represented by y as a function of x).

Inputs to x and y must typically be of the same length. If x but not y is provided, xy.coords from grDevices is used to determine x- and y-values.

The risk events quantified by the cumulative risk values in y are assumed to be uni-directional, non-reversible, and expressed as percentages (ranging from 0 to 100). Thus, an element in the population can only switch its status once (from 'unaffected' to 'affected' by the risk factor).

A cumulative risk increment is computed for an interval ranging from x_from to x_to. If risk values for x_from or x_to are not provided (i.e., in x and y), a curve is fitted to predict y by x (by fit_curve = TRUE).

Note that naive interpretations allow for both overestimation (e.g., reading off population values) and underestimation (e.g., reading off future risk increases without re-scaling to remaining population).

For instructional purposes, plot_crisk provides options for showing/hiding various elements required for computing or comprehending cumulative risk increments.

Color information is based on a vector with named colors col_pal = pal_crisk.

See also

pal_crisk corresponding color palette.

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

Examples

# Data:
x <- seq(0, 100, by = 10)
y <- c(0, 0, 0, 8, 24, 50, 70, 80, 83, 85, 85)

# Basic versions:
plot_crisk(x, y)  # using data provided

plot_crisk(x, y, x_from = 40)  # use and mark 1 provided point

plot_crisk(x, y, x_from = 44)  # use and mark 1 predicted point
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.

plot_crisk(x, y, x_from = 40, x_to = 60)  # use 2 provided points

plot_crisk(x, y, x_from = 44, x_to = 64)  # use 2 predicted points
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.

plot_crisk(x, y, fit_curve = TRUE)  # fitting curve to provided data


# Training versions:
plot_crisk(x, y, 44, 64, show_pas = TRUE)  # past/passed risk only
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.

plot_crisk(x, y, 44, 64, show_rem = TRUE)  # remaining risk only
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.

plot_crisk(x, y, 44, 64, show_pas = TRUE, show_rem = TRUE) # both risks
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.

plot_crisk(x, y, 44, 64, show_aux = TRUE)  # auxiliary lines + axis
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.

plot_crisk(x, y, 44, 64, show_aux = TRUE, show_pop = TRUE)  # + population parts
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.

plot_crisk(x, y, 44, 64, show_aux = TRUE, show_num = TRUE)  # + numeric values
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.

plot_crisk(x, y, 44, 85, show_aux = TRUE, show_pop = TRUE, show_num = TRUE) # + aux/pop/num
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.


# Note: Showing ALL is likely to overplot/overwhelm:
plot_crisk(x, y, x_from = 47, x_to = 67, fit_curve = TRUE,
           main = "The main title", sub = "Some subtitle",
           show_pas = TRUE, show_rem = TRUE, show_aux = TRUE, show_pop = TRUE,
           show_num = TRUE, show_inc = TRUE, show_grid = TRUE, mar_notes = TRUE)


# Small x- and y-values and linear increases:
plot_crisk(x = 2:10, y = seq(12, 28, by = 2), x_from = 4.5, x_to = 8.5,
           show_pas = TRUE, show_rem = TRUE, show_aux = TRUE, show_pop = TRUE,
           show_num = TRUE, show_inc = TRUE)
#> plot_crisk: x_from is not in x: Using fit_curve = TRUE.