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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lift.R
\name{lift}
\alias{lift}
\alias{lift.formula}
\alias{lift.default}
\alias{xyplot.lift}
\alias{print.lift}
\alias{ggplot.lift}
\title{Lift Plot}
\usage{
lift(x, ...)
\method{lift}{default}(x, ...)
\method{lift}{formula}(
x,
data = NULL,
class = NULL,
subset = TRUE,
lattice.options = NULL,
cuts = NULL,
labels = NULL,
...
)
\method{print}{lift}(x, ...)
\method{xyplot}{lift}(x, data = NULL, plot = "gain", values = NULL, ...)
\method{ggplot}{lift}(
data = NULL,
mapping = NULL,
plot = "gain",
values = NULL,
...,
environment = NULL
)
}
\arguments{
\item{x}{a \code{lattice} formula (see \code{\link[lattice:xyplot]{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.}
\item{\dots}{options to pass through to \code{\link[lattice:xyplot]{xyplot}}
or the panel function (not used in \code{lift.formula}).}
\item{data}{For \code{lift.formula}, a data frame (or more precisely,
anything that is a valid \code{envir} argument in \code{eval}, e.g., a list
or an environment) containing values for any variables in the formula, as
well as \code{groups} and \code{subset} if applicable. If not found in
\code{data}, or if \code{data} is unspecified, the variables are looked for
in the environment of the formula. This argument is not used for
\code{xyplot.lift} or \code{ggplot.lift}.}
\item{class}{a character string for the class of interest}
\item{subset}{An expression that evaluates to a logical or integer indexing
vector. It is evaluated in \code{data}. Only the resulting rows of
\code{data} are used for the plot.}
\item{lattice.options}{A list that could be supplied to
\code{\link[lattice:lattice.options]{lattice.options}}}
\item{cuts}{If a single value is given, a sequence of values between 0 and 1
are created with length \code{cuts}. If a vector, these values are used as
the cuts. If \code{NULL}, each unique value of the model prediction is used.
This is helpful when the data set is large.}
\item{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.}
\item{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.}
\item{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 \code{plot.line} or \code{superpose.line}
component of the current lattice theme to draw the lines (depending on
whether groups were used. These values are only used when \code{type =
"gain"}.}
\item{mapping, environment}{Not used (required for \code{ggplot} consistency).}
}
\value{
\code{lift.formula} returns a list with elements: \item{data}{the
data used for plotting} \item{cuts}{the number of cuts} \item{class}{the
event class} \item{probNames}{the names of the model probabilities}
\item{pct}{the baseline event rate}
\code{xyplot.lift} returns a \pkg{lattice} object
}
\description{
For classification models, this function creates a 'lift plot' that
describes how well a model ranks samples for one class
}
\details{
\code{lift.formula} is used to process the data and \code{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:
\enumerate{ \item The data are ordered by the numeric model prediction used
on the right-hand side of the model formula \item Each unique value of the
score is treated as a cut point \item The number of samples with true
results equal to \code{class} are determined \item The lift is calculated as
the ratio of the percentage of samples in each split corresponding to
\code{class} over the same percentage in the entire data set} \code{lift}
with \code{plot = "gain"} produces a plot of the cumulative lift values by
the percentage of samples evaluated while \code{plot = "lift"} shows the cut
point value versus the lift statistic.
This implementation uses the \pkg{lattice} function
\code{\link[lattice:xyplot]{xyplot}}, so plot elements can be changed via
panel functions, \code{\link[lattice:trellis.par.get]{trellis.par.set}} or
other means. \code{lift} uses the panel function \code{\link{panel.lift2}}
by default, but it can be changes using
\code{\link[lattice:update.trellis]{update.trellis}} (see the examples in
\code{\link{panel.lift2}}).
The following elements are set by default in the plot but can be changed by
passing new values into \code{xyplot.lift}: \code{xlab = "\% Samples
Tested"}, \code{ylab = "\% Samples Found"}, \code{type = "S"}, \code{ylim =
extendrange(c(0, 100))} and \code{xlim = extendrange(c(0, 100))}.
}
\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, auto.key = list(columns = 2), value = c(10, 30))
xyplot(lift2, plot = "lift", auto.key = list(columns = 2))
}
\seealso{
\code{\link[lattice:xyplot]{xyplot}},
\code{\link[lattice:trellis.par.get]{trellis.par.set}}
}
\author{
Max Kuhn, some \pkg{lattice} code and documentation by Deepayan
Sarkar
}
\keyword{hplot}
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