backup/xyplot.cv: X-Y plots of cross-validation results

In cvTools: Cross-Validation Tools for Regression Models

X-Y plots of cross-validation results

Description

Plot the (average) results from (repeated) K-fold cross-validation on the y-axis against the respective models on the x-axis.

Usage

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

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

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

Arguments

Details

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

In most situations, the default behavior is to represent the cross-validation results for each model by a vertical line segment (i.e., to call the default method of xyplot with type = "h"). However, the behavior is different for objects of class "cvTuning" with only one numeric tuning parameter. In that situation, the cross-validation results are plotted against the values of the tuning parameter as a connected line (i.e., by using type = "b").

The default behavior can of course be overridden by supplying the type argument (a full list of accepted values can be found in the help file of panel.xyplot).

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, dotplot, 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
xyplot(cvFits)