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
)

x

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`

).

data

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`

.

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 `data`

. Only the resulting rows of
`data`

are used for the plot.

lattice.options

A list that could be supplied to
`lattice.options`

cuts

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.

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 `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"
```

.

mapping, environment

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 determinedThe 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)) # }