cvTools (version 0.3.2)

bwplot.cv: Box-and-whisker plots of cross-validation results

Description

Produce box-and-whisker plots of results from repeated \(K\)-fold cross-validation.

Usage

# S3 method for cv
bwplot (x, data, select = NULL, ...)

# S3 method for cvSelect bwplot (x, data, subset = NULL, select = NULL, ...)

Value

An object of class "trellis" is returned invisibly. The

update method can be used to update components of the object and the

print method (usually called by default) will plot it on an appropriate plotting device.

Arguments

x

an object inheriting from class "cv" or "cvSelect" that contains cross-validation results.

data

currently ignored.

subset

a character, integer or logical vector indicating the subset of models for which to plot the cross-validation results.

select

a character, integer or logical vector indicating the columns of cross-validation results to be plotted.

...

additional arguments to be passed to the "formula" method of bwplot.

Author

Andreas Alfons

Details

For objects with multiple columns of repeated cross-validation results, conditional box-and-whisker plots are produced.

See Also

cvFit, cvSelect, cvTuning, plot, densityplot, xyplot, dotplot

Examples

Run this code
library("robustbase")
data("coleman")
set.seed(1234)  # set seed for reproducibility

## set up folds for cross-validation
folds <- cvFolds(nrow(coleman), K = 5, R = 50)


## compare LS, MM and LTS regression

# perform cross-validation for an LS regression model
fitLm <- lm(Y ~ ., data = coleman)
cvFitLm <- cvLm(fitLm, cost = rtmspe, 
    folds = folds, trim = 0.1)

# perform cross-validation for an MM regression model
fitLmrob <- lmrob(Y ~ ., data = coleman, k.max = 500)
cvFitLmrob <- cvLmrob(fitLmrob, cost = rtmspe, 
    folds = folds, trim = 0.1)

# perform cross-validation for an LTS regression model
fitLts <- ltsReg(Y ~ ., data = coleman)
cvFitLts <- cvLts(fitLts, cost = rtmspe, 
    folds = folds, trim = 0.1)

# combine results into one object
cvFits <- cvSelect(LS = cvFitLm, MM = cvFitLmrob, LTS = cvFitLts)
cvFits

# plot results for the MM regression model
bwplot(cvFitLmrob)
# plot combined results
bwplot(cvFits)


## compare raw and reweighted LTS estimators for 
## 50% and 75% subsets

# 50% subsets
fitLts50 <- ltsReg(Y ~ ., data = coleman, alpha = 0.5)
cvFitLts50 <- cvLts(fitLts50, cost = rtmspe, folds = folds, 
    fit = "both", trim = 0.1)

# 75% subsets
fitLts75 <- ltsReg(Y ~ ., data = coleman, alpha = 0.75)
cvFitLts75 <- cvLts(fitLts75, cost = rtmspe, folds = folds, 
    fit = "both", trim = 0.1)

# combine and plot results
cvFitsLts <- cvSelect("0.5" = cvFitLts50, "0.75" = cvFitLts75)
cvFitsLts
bwplot(cvFitsLts)

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