cvTools (version 0.3.3)

aggregate.cv: Aggregate cross-validation results

Description

Compute summary statistics of results from repeated \(K\)-fold cross-validation.

Usage

# S3 method for cv
aggregate(x, FUN = mean, select = NULL, ...)
# S3 method for cvSelect
aggregate(x, FUN = mean, select = NULL, ...)
# S3 method for cvTuning
aggregate(x, ...)

Value

The "cv" method returns a vector or matrix of aggregated cross-validation results, depending on whether FUN returns a single value or a vector.

For the other methods, a data frame containing the aggregated cross-validation results for each model is returned. In the case of the "cvTuning" method, the data frame contains the combinations of tuning parameters rather than a column describing the models.

Arguments

x

an object inheriting from class "cv" or "cvSelect" that contains cross-validation results (note that the latter includes objects of class "cvTuning").

FUN

a function to compute the summary statistics.

select

a character, integer or logical vector indicating the columns of cross-validation results for which to compute the summary statistics.

...

for the "cvTuning" method, additional arguments to be passed to the "cvSelect" method. Otherwise additional arguments to be passed to FUN.

Author

Andreas Alfons

See Also

cvFit, cvSelect, cvTuning, aggregate

Examples

# \donttest{
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 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 results into one object
cvFitsLts <- cvSelect("0.5" = cvFitLts50, "0.75" = cvFitLts75)
cvFitsLts

# summary of the results with the 50% subsets
aggregate(cvFitLts50, summary)
# summary of the combined results
aggregate(cvFitsLts, summary)
# }