R: Plot a cumulative risk curve.

plot_crisk {riskyr}

R Documentation

Plot a cumulative risk curve.

Description

plot_crisk creates visualizations of cumulative risks.

Usage

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.

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.

Value

Nothing (NULL).

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, 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, 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, y, 44, 64, show_rem = TRUE)  # remaining risk only
plot_crisk(x, y, 44, 64, show_pas = TRUE, show_rem = TRUE) # both risks
plot_crisk(x, y, 44, 64, show_aux = TRUE)  # auxiliary lines + axis
plot_crisk(x, y, 44, 64, show_aux = TRUE, show_pop = TRUE)  # + population parts
plot_crisk(x, y, 44, 64, show_aux = TRUE, show_num = TRUE)  # + numeric values
plot_crisk(x, y, 44, 85, show_aux = TRUE, show_pop = TRUE, show_num = TRUE) # + aux/pop/num

# 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)

[Package riskyr version 0.4.0 Index]