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.
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)