For classification models, this function creates a 'lift plot' that describes how well a model ranks samples for one class
## 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, ...)
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
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
- 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
- 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.
- 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 the lift plot, 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 unque 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
liftproduces a plot of the cumulative lift values by the percentage of samples evaluated.
This implementation uses the
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)).
lift.formulareturns 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
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))