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

In cvTools: Cross-Validation Tools for Regression Models

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

Details

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

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.

Author(s)

Andreas Alfons

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 = 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 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)