R: Plot the cumulative gain curve of a sort-order with extra...

GainCurvePlotWithNotation {WVPlots}

R Documentation

Plot the cumulative gain curve of a sort-order with extra notation

Description

Plot the cumulative gain curve of a sort-order with extra notation.

Usage

GainCurvePlotWithNotation(
  frame,
  xvar,
  truthVar,
  title,
  gainx,
  labelfun,
  ...,
  sort_by_model = TRUE,
  estimate_sig = FALSE,
  large_count = 1000,
  model_color = "darkblue",
  wizard_color = "darkgreen",
  shadow_color = "darkgray",
  crosshair_color = "red",
  text_color = "black"
)

Arguments

`frame`	data frame to get values from
`xvar`	name of the independent (input or model score) column in frame
`truthVar`	name of the dependent (output or result to be modeled) column in frame
`title`	title to place on plot
`gainx`	the point on the x axis corresponding to the desired label
`labelfun`	a function to return a label for the marked point
`...`	no unnamed argument, added to force named binding of later arguments.
`sort_by_model`	logical, if TRUE use the model to calculate gainy, else use wizard.
`estimate_sig`	logical, if TRUE compute significance
`large_count`	numeric, upper bound target for number of plotting points
`model_color`	color for the model curve
`wizard_color`	color for the "wizard" (best possible) curve
`shadow_color`	color for the shaded area under the curve
`crosshair_color`	color for the annotation location lines
`text_color`	color for the annotation text

Details

This is the standard gain curve plot (see GainCurvePlot) with a label attached to a particular value of x. The label is created by a function labelfun, which takes as inputs the x and y coordinates of a label and returns a string (the label).

By default, uses the model to calculate the y value of the calculated point; to use the wizard curve, set sort_by_model = FALSE

Examples


if (requireNamespace('data.table', quietly = TRUE)) {
	# don't multi-thread during CRAN checks
		data.table::setDTthreads(1)
}

set.seed(34903490)
y = abs(rnorm(20)) + 0.1
x = abs(y + 0.5*rnorm(20))
frm = data.frame(model=x, value=y)
gainx = 0.25  # get the predicted top 25% most valuable points as sorted by the model
# make a function to calculate the label for the annotated point
labelfun = function(gx, gy) {
  pctx = gx*100
  pcty = gy*100

  paste("The predicted top ", pctx, "% most valuable points by the model\n",
        "are ", pcty, "% of total actual value", sep='')
}
WVPlots::GainCurvePlotWithNotation(frm, "model", "value",
   title="Example Gain Curve with annotation",
   gainx=gainx,labelfun=labelfun)

# now get the top 25% actual most valuable points

labelfun = function(gx, gy) {
  pctx = gx*100
  pcty = gy*100

  paste("The actual top ", pctx, "% most valuable points\n",
        "are ", pcty, "% of total actual value", sep='')
}

WVPlots::GainCurvePlotWithNotation(frm, "model", "value",
   title="Example Gain Curve with annotation",
   gainx=gainx,labelfun=labelfun, sort_by_model=FALSE)