cvTools (version 0.1.1)

densityplot.cv: Kernel density plots of cross-validation results

Description

Produce kernel density plots of results from repeated $K$-fold cross-validation.

Usage

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

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 densityplot.

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 kernel density plots are produced.

See Also

cvFit, cvSelect, cvTuning, plot, bwplot, xyplot, dotplot

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 = 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
densityplot(cvFitLmrob)
# plot combined results
densityplot(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
densityplot(cvFitsLts)