Plot the cumulative gain curve of a sort-order.

```
GainCurvePlot(
frame,
xvar,
truthVar,
title,
...,
estimate_sig = FALSE,
large_count = 1000,
truth_target = NULL,
model_color = "darkblue",
wizard_color = "darkgreen",
shadow_color = "darkgray"
)
```

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

...

no unnamed argument, added to force named binding of later arguments.

estimate_sig

logical, if TRUE compute significance.

large_count

numeric, upper bound target for number of plotting points.

truth_target

if not NULL compare to this scalar value.

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

The use case for this visualization is to compare a predictive model score to an actual outcome (either binary (0/1) or continuous). In this case the gain curve plot measures how well the model score sorts the data compared to the true outcome value.

The x-axis represents the fraction of items seen when sorted by score, and the y-axis represents the cumulative summed true outcome represented by the items seen so far. See, for example, https://www.ibm.com/support/knowledgecenter/SSLVMB_24.0.0/spss/tutorials/mlp_bankloan_outputtype_02.html.

For comparison, `GainCurvePlot`

also plots the "wizard curve": the gain curve when the
data is sorted according to its true outcome.

To improve presentation quality, the plot is limited to approximately `large_count`

points (default: 1000).
For larger data sets, the data is appropriately randomly sampled down before plotting.

# NOT RUN { set.seed(34903490) y = abs(rnorm(20)) + 0.1 x = abs(y + 0.5*rnorm(20)) frm = data.frame(model=x, value=y) WVPlots::GainCurvePlot(frm, "model", "value", title="Example Continuous Gain Curve") # }