cvTools (version 0.1.1)

summary.cv: 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

object

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

...

currently ignored.

Value

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

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)