perryPlot {perry} | R Documentation |
Plot resampling-based prediction error results
Description
Plot results of resampling-based prediction error measures.
Usage
perryPlot(object, ...)
## S3 method for class 'perry'
perryPlot(
object,
which = c("box", "density", "dot"),
select = NULL,
seFactor = NA,
...
)
## S3 method for class 'perrySelect'
perryPlot(
object,
which = c("box", "density", "dot", "line"),
subset = NULL,
select = NULL,
seFactor = object$seFactor,
...
)
## S3 method for class 'setupPerryPlot'
perryPlot(object, mapping = object$mapping, facets = object$facets, ...)
## S3 method for class 'perry'
autoplot(object, ...)
## S3 method for class 'perrySelect'
autoplot(object, ...)
## S3 method for class 'perry'
plot(x, ...)
## S3 method for class 'perrySelect'
plot(x, ...)
Arguments
object , x |
an object inheriting from class |
... |
additional arguments to be passed down, eventually to
|
which |
a character string specifying the type of plot. Possible
values are |
select |
a character, integer or logical vector indicating the columns of prediction error results to be plotted. |
seFactor |
a numeric value giving the multiplication factor of the
standard error for displaying error bars in dot plots or line plots. Error
bars in those plots can be suppressed by setting this to |
subset |
a character, integer or logical vector indicating the subset of models for which to plot the prediction error results. |
mapping |
an aesthetic mapping to override the default behavior (see
|
facets |
a faceting formula to override the default behavior. If
supplied, |
Details
For objects with multiple columns of prediction error results, conditional plots are produced.
Value
An object of class "ggplot"
(see ggplot
).
Note
Duplicate indices in subset
or select
are removed such
that all models and prediction error results are unique.
Author(s)
Andreas Alfons
See Also
perryFit
, perrySelect
,
perryTuning
,
Examples
library("perryExamples")
data("coleman")
set.seed(1234) # set seed for reproducibility
## set up folds for cross-validation
folds <- cvFolds(nrow(coleman), K = 5, R = 10)
## compare LS, MM and LTS regression
# perform cross-validation for an LS regression model
fitLm <- lm(Y ~ ., data = coleman)
cvLm <- perry(fitLm, splits = folds,
cost = rtmspe, trim = 0.1)
# perform cross-validation for an MM regression model
fitLmrob <- lmrob(Y ~ ., data = coleman, maxit.scale = 500)
cvLmrob <- perry(fitLmrob, splits = folds,
cost = rtmspe, trim = 0.1)
# perform cross-validation for an LTS regression model
fitLts <- ltsReg(Y ~ ., data = coleman)
cvLts <- perry(fitLts, splits = folds,
cost = rtmspe, trim = 0.1)
# combine results into one object
cv <- perrySelect(LS = cvLm, MM = cvLmrob, LTS = cvLts,
.selectBest = "min")
cv
# plot results for the MM regression model
plot(cvLmrob, which = "box")
plot(cvLmrob, which = "density")
plot(cvLmrob, which = "dot", seFactor = 2)
# plot combined results
plot(cv, which = "box")
plot(cv, which = "density")
plot(cv, which = "dot", seFactor = 2)