Description Usage Arguments Details Value Author(s) See Also Examples
For classification models, this function creates a 'lift plot' that describes how well a model ranks samples for one class
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  lift(x, ...)
## Default S3 method:
lift(x, ...)
## S3 method for class 'formula'
lift(
x,
data = NULL,
class = NULL,
subset = TRUE,
lattice.options = NULL,
cuts = NULL,
labels = NULL,
...
)
## S3 method for class 'lift'
print(x, ...)
## S3 method for class 'lift'
xyplot(x, data = NULL, plot = "gain", values = NULL, ...)
## S3 method for class 'lift'
ggplot(
data = NULL,
mapping = NULL,
plot = "gain",
values = NULL,
...,
environment = NULL
)

x 
a 
... 
options to pass through to 
data 
For 
class 
a character string for the class of interest 
subset 
An expression that evaluates to a logical or integer indexing
vector. It is evaluated in 
lattice.options 
A list that could be supplied to

cuts 
If a single value is given, a sequence of values between 0 and 1
are created with length 
labels 
A named list of labels for keys. The list should have an element for each term on the righthand side of the formula and the names should match the names of the models. 
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 cutoff versus the lift statistic. 
values 
A vector of numbers between 0 and 100 specifying reference
values for the percentage of samples found (i.e. the yaxis). Corresponding
points on the xaxis 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 
mapping, environment 
Not used (required for 
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 righthand 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))
.
lift.formula
returns 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 
xyplot.lift
returns a lattice object
Max Kuhn, some lattice code and documentation by Deepayan Sarkar
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  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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