backup/dotplot.cv: Dot plots of cross-validation results

In cvTools: Cross-Validation Tools for Regression Models

Dot plots of cross-validation results

Description

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

Usage

  ## S3 method for class 'cv'
 dotplot(x, data, select = NULL,
    seFactor = NA, ...)

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

Arguments

Details

For objects with multiple columns of repeated cross-validation results, conditional dot 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, 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)