# panel.lift2: Lattice Panel Functions for Lift Plots In caret: Classification and Regression Training

## Description

Two panel functions that be used in conjunction with `lift`.

## Usage

 `1` ```panel.lift2(x, y, pct = 0, values = NULL, ...) ```

## Arguments

 `x` the percentage of searched to be plotted in the scatterplot `y` the percentage of events found to be plotted in the scatterplot `pct` the baseline percentage of true events in the data `values` A vector of numbers between 0 and 100 specifying reference values for the percentage of samples found (i.e. the y-axis). Corresponding points on the x-axis are found via interpolation and line segments are shown to indicate how many samples must be tested before these percentages are found. The lines use either the `plot.line` or `superpose.line` component of the current lattice theme to draw the lines (depending on whether groups were used `...` options to pass to `panel.xyplot`

## Details

`panel.lift` plots the data with a simple (black) 45 degree reference line.

`panel.lift2` is the default for `lift` and plots the data points with a shaded region encompassing the space between to the random model and perfect model trajectories. The color of the region is determined by the lattice `reference.line` information (see example below).

## Author(s)

Max Kuhn

`lift`, `panel.xyplot`, `xyplot`, trellis.par.set
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```set.seed(1) simulated <- data.frame(obs = factor(rep(letters[1:2], each = 100)), perfect = sort(runif(200), decreasing = TRUE), random = runif(200)) regionInfo <- trellis.par.get("reference.line") regionInfo\$col <- "lightblue" trellis.par.set("reference.line", regionInfo) lift2 <- lift(obs ~ random + perfect, data = simulated) lift2 xyplot(lift2, auto.key = list(columns = 2)) ## use a different panel function xyplot(lift2, panel = panel.lift) ```