cvTools (version 0.1.1)

dotplot.cvSelect: Dot plots of cross-validation results

Description

Produce dot plots of (average) results from (repeated) $K$-fold cross-validation.

Usage

## S3 method for class 'cvSelect':
dotplot(x, data, subset = NULL, select
     = NULL, ...)

Arguments

x

an object inheriting from class "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 dotplot.

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.

Details

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

See Also

cvFit, cvSelect, cvTuning, plot, xyplot, bwplot, densityplot

Examples

library("robustbase")
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)
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 and plot results
cvFits <- cvSelect(LS = cvFitLm, MM = cvFitLmrob, LTS = cvFitLts)
cvFits
dotplot(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
dotplot(cvFitsLts)