Description Usage Arguments Value Note Author(s) See Also Examples

View source: R/setupPerryPlot.R

Extract and prepare the relevant information for a plot of results of resampling-based prediction error measures.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
setupPerryPlot(object, ...)
## S3 method for class 'perry'
setupPerryPlot(
object,
which = c("box", "density", "dot"),
select = NULL,
seFactor = NA,
...
)
## S3 method for class 'perrySelect'
setupPerryPlot(
object,
which = c("box", "density", "dot", "line"),
subset = NULL,
select = NULL,
seFactor = object$seFactor,
...
)
## S3 method for class 'perryTuning'
setupPerryPlot(object, ...)
``` |

`object` |
an object inheriting from class |

`...` |
for the |

`which` |
a character string specifying the type of plot to
prepare. Possible values are |

`select` |
a character, integer or logical vector indicating the columns of prediction error results to be prepared for plotting. |

`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 to be prepared for plotting. |

An object of class `"setupPerryPlot"`

with the following
components:

`data`

a data frame containing the following columns:

`Fit`

a vector or factor containing the identifiers of the models.

`Name`

a factor containing the names of the predictor error results (not returned in case of only one column of prediction error results with the default name).

`PE`

the estimated prediction errors.

`Lower`

the lower end points of the error bars (only returned if possible to compute).

`Upper`

the upper end points of the error bars (only returned if possible to compute).

`which`

a character string specifying the type of plot.

`grouped`

a logical indicating whether density plots should be grouped due to multiple model fits (only returned in case of density plots for the

`"perrySelect"`

and`"perryTuning"`

methods).`includeSE`

a logical indicating whether error bars based on standard errors are available (only returned in case of dot plots or line plots).

`mapping`

default aesthetic mapping for the plots.

`facets`

default faceting formula for the plots (not returned in case of only one column of prediction error results with the default name).

`tuning`

a data frame containing the grid of tuning parameter values for which the prediction error was estimated (only returned for the

`"perryTuning"`

method).

Duplicate indices in `subset`

or `select`

are removed such
that all models and prediction error results are unique.

Andreas Alfons

`perryFit`

, `perrySelect`

,
`perryTuning`

,

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 33 34 35 36 37 38 39 40 41 42 43 44 | ```
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
## convert MM regression results to data frame for plotting
# all replications for box plot
cvLmrobBox <- setupPerryPlot(cvLmrob, which = "box")
perryPlot(cvLmrobBox)
# aggregated results for dot plot
cvLmrobDot <- setupPerryPlot(cvLmrob, which = "dot", seFactor = 2)
perryPlot(cvLmrobDot)
## convert combined results to data frame for plotting
# all replications for box plot
cvBox <- setupPerryPlot(cv, which = "box")
perryPlot(cvBox)
# aggregated results for dot plot
cvDot <- setupPerryPlot(cv, which = "dot", seFactor = 2)
perryPlot(cvDot)
``` |

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