Summarize cross-validation results

Description

Produce a summary of results from (repeated) K-fold cross-validation.

Usage

  ## S3 method for class 'cv'
 summary(object, ...)

  ## S3 method for class 'cvSelect'
 summary(object, ...)

  ## S3 method for class 'cvTuning'
 summary(object, ...)

Arguments

Value

An object of class "summary.cv", "summary.cvSelect" or "summary.cvTuning", depending on the class of object.

Author(s)

Andreas Alfons

See Also

cvFit, cvSelect, cvTuning, summary

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
summary(cvFitLts50)
# summary of the combined results
summary(cvFitsLts)


## evaluate MM regression models tuned for 
## 80%, 85%, 90% and 95% efficiency
tuning <- list(tuning.psi=c(3.14, 3.44, 3.88, 4.68))

# set up function call
call <- call("lmrob", formula = Y ~ .)
# perform cross-validation
cvFitsLmrob <- cvTuning(call, data = coleman, 
    y = coleman$Y, tuning = tuning, cost = rtmspe, 
    folds = folds, costArgs = list(trim = 0.1))
cvFitsLmrob

# summary of results
summary(cvFitsLmrob)