backup/aggregate.cv: Aggregate cross-validation results

In cvTools: Cross-Validation Tools for Regression Models

Aggregate cross-validation results

Description

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

Usage

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

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

  ## S3 method for class 'cvTuning'
 aggregate(x, ...)

Arguments

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.

Author(s)

Andreas Alfons

See Also

cvFit, cvSelect, cvTuning, aggregate

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 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)