For classification models, this function creates a 'lift plot' that describes how well a model ranks samples for one class
lift(x, ...)"lift"(x, data = NULL, class = NULL, subset = TRUE, lattice.options = NULL, cuts = NULL, labels = NULL, ...)"xyplot"(x, data, plot = "gain", values = NULL, ...)
xyplotfor 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 columns corresponding to a numeric ranking variable for a model (e.g. class probabilities). The classification variable should have two levels.
lift.formula, a data frame (or more precisely, anything that is a valid
eval, e.g., a list or an environment) containing values for any variables in the formula, as well as
subsetif applicable. If not found in
data, or if
datais unspecified, the variables are looked for in the environment of the formula. This argument is not used for
- a character string for the class of interest
An expression that evaluates to a logical or integer indexing vector. It is evaluated in
data. Only the resulting rows of
dataare used for the plot.
A list that could be supplied to
If a single value is given, a sequence of values between 0 and 1 are created with length
cuts. If a vector, these values are used as the cuts. If
NULL, each unique value of the model prediction is used. This is helpful when the data set is large.
- 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.
- 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.
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
superpose.linecomponent of the current lattice theme to draw the lines (depending on whether groups were used. These values are only used when
type = "gain".
options to pass through to
xyplotor the panel function (not used in
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:
- The data are ordered by the numeric model prediction used on the right-hand side of the model formula
- Each unique value of the score is treated as a cut point
- The number of samples with true results equal to
- The lift is calculated as the ratio of the percentage of samples in each split corresponding to
classover the same percentage in the entire data set
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
The following elements are set by default in the plot but can be changed by passing new values into
xlab = "% Samples Tested",
ylab = "% Samples Found",
type = "S",
ylim = extendrange(c(0, 100)) and
xlim = extendrange(c(0, 100)).
- the data used for plotting
- the number of cuts
- the event class
- the names of the model probabilities
- the baseline event rate
lift.formulareturns a list with elements:
xyplot.liftreturns a lattice object
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, auto.key = list(columns = 2), value = c(10, 30)) xyplot(lift2, plot = "lift", auto.key = list(columns = 2))