For classification models, this function creates a 'lift plot' that describes how well a model ranks samples for one class
lift(x, ...)# S3 method for default
lift(x, ...)
# S3 method for formula
lift(
x,
data = NULL,
class = NULL,
subset = TRUE,
lattice.options = NULL,
cuts = NULL,
labels = NULL,
...
)
# S3 method for lift
print(x, ...)
# S3 method for lift
xyplot(x, data = NULL, plot = "gain", values = NULL, ...)
# S3 method for lift
ggplot(
data = NULL,
mapping = NULL,
plot = "gain",
values = NULL,
...,
environment = NULL
)
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 columns corresponding to a numeric ranking variable for a model (e.g.
class probabilities). The classification variable should have two levels.
options to pass through to xyplot
or the panel function (not used in lift.formula).
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 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 or ggplot.lift.
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
data are used for the plot.
A list that could be supplied to
lattice.options
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 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".
Not used (required for ggplot consistency).
lift.formula returns a list with elements:
the data used for plotting
the number of cuts
the event class
the names of the model probabilities
the baseline event rate
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:
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 class are determined
The lift is calculated as
the ratio of the percentage of samples in each split corresponding to
class over 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)).
# NOT RUN {
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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