lift(x, ...)
"lift"(x, data = NULL, class = NULL, subset = TRUE, lattice.options = NULL, cuts = NULL, labels = NULL, ...)
"xyplot"(x, data, plot = "gain", values = NULL, ...)
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 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 envir
argument in eval
, e.g., a list or an environment) containing values for any variables in the formula, as well as groups
and subset
if applicable. If not found in data
, or if data
is unspecified, the variables are looked for in the environment of the formula. This argument is not used for xyplot.lift
.
data
. Only the resulting rows of data
are used for the plot.
lattice.options
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.
plot.line
or superpose.line
component of the current lattice theme to draw the lines (depending on whether groups were used. These values are only used when type = "gain"
.xyplot
or the panel function (not used in lift.formula
).
lift.formula
returns a list with elements:
xyplot.lift
returns a lattice object
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:
class
are determined
class
over the same percentage in the entire data setlift
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))
.
xyplot
, trellis.par.set
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))
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