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 |
x |
a |
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 |
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. |
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. |
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 |
... |
options to pass through to |
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))
.
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))
|
Loading required package: lattice
Loading required package: ggplot2
Call:
lift.formula(x = obs ~ random, data = simulated)
Models: random
Event: a (50%)
Call:
lift.formula(x = obs ~ random + perfect, data = simulated)
Models: random, perfect
Event: a (50%)
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