caret (version 5.17-7)

lift: Lift Plot

Description

For classification models, this function creates a 'lift plot' that describes how well a model ranks samples for one class

Usage

lift(x, ...)
## S3 method for class 'formula':
lift(x, data = NULL, class = NULL,
     subset = TRUE, lattice.options = NULL, labels = NULL, 
     ...)
## S3 method for class 'lift':
xyplot(x, data, plot = "gain", ...)

Arguments

x

a lattice formula (see xyplot for syntax) where the left-hand side of the formula is a factor class variable of the observed outcome and the right-hand side specifies one or model

data

For lift.formula, a data frame (or more precisely, anything that is a valid envir argument in eval, e.g., a list or an environment) containing values for any variables in the formula, as well as groups a

class

a character string for the class of interest

subset

An expression that evaluates to a logical or integer indexing vector. It is evaluated in data. Only the resulting rows of data are used for the plot.

lattice.options

A list that could be supplied to lattice.options

labels

A named list of labels for keys. The list should have an element for each term on the right-hand side of the formula and the names should match the names of the models.

plot

Either "gain" (the default) or "lift". The former plots the number of samples called events versus the event rate while the latter shows the event cut-off versus the lift statistic.

...

options to pass through to xyplot or the panel function (not used in lift.formula).

Value

• lift.formula returns a list with elements:
• data
  the data used for plotting
• cuts
  the number of cuts
• class
  the event class
• probNames
  the names of the model probabilities
• pct
  the baseline event rate
• xyplot.lift returns a lattice object

Details

lift.formula is used to process the data and xyplot.lift is used to create the plot.

To construct data for the the lift and gain plots, the following steps are used for each model:

1. The data are ordered by the numeric model prediction used on the right-hand side of the model formula
2. Each unque value of the score is treated as a cut point
3. The number of samples with true results equal toclassare determined
4. The lift is calculated as the ratio of the percentage of samples in each split corresponding toclassover the same percentage in the entire data set

lift with plot = "gain" produces a plot of the cumulative lift values by the percentage of samples evaluated while plot = "lift" shows the cut point value versus the lift statistic.

This implementation uses the lattice function xyplot, so plot elements can be changed via panel functions, trellis.par.set or other means. lift uses the panel function panel.lift2 by default, but it can be changes using update.trellis (see the examples in panel.lift2).

The following elements are set by default in the plot but can be changed by passing new values into xyplot.lift: xlab = "% Samples Tested", ylab = "% Samples Found", type = "S", ylim = extendrange(c(0, 100)) and xlim = extendrange(c(0, 100)).

See Also

xyplot, trellis.par.set

Examples

set.seed(1)
simulated <- data.frame(obs = factor(rep(letters[1:2], each = 100)),
                        perfect = sort(runif(200), decreasing = TRUE),
                        random = runif(200))

lift1 <- lift(obs ~ random, data = simulated)
lift1
xyplot(lift1)

lift2 <- lift(obs ~ random + perfect, data = simulated)
lift2
xyplot(lift2, auto.key = list(columns = 2))

xyplot(lift2, plot = "lift", auto.key = list(columns = 2))