R: Access or set information on cross-validation results
accessors
R Documentation
Access or set information on cross-validation results
Description
Retrieve or set the names of cross-validation results,
retrieve or set the identifiers of the models, or
retrieve the number of cross-validation results or
included models.
Usage
cvNames(x)
cvNames(x) <- value
fits(x)
fits(x) <- value
ncv(x)
nfits(x)
Arguments
x
an object inheriting from class "cv" or
"cvSelect" that contains cross-validation
results.
value
a vector of replacement values.
Value
cvNames returns the names of the cross-validation
results. The replacement function thereby returns them
invisibly.
fits returns the identifiers of the models for
objects inheriting from class "cvSelect" and
NULL for objects inheriting from class
"cv". The replacement function thereby returns
those values invisibly.
ncv returns the number of cross-validation
results.
nfits returns the number of models included in
objects inheriting from class "cvSelect" and
NULL for objects inheriting from class
"cv".
Author(s)
Andreas Alfons
See Also
cvFit, cvSelect,
cvTuning
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
# "cv" object
ncv(cvFitLts50)
nfits(cvFitLts50)
cvNames(cvFitLts50)
cvNames(cvFitLts50) <- c("improved", "initial")
fits(cvFitLts50)
cvFitLts50
# "cvSelect" object
ncv(cvFitsLts)
nfits(cvFitsLts)
cvNames(cvFitsLts)
cvNames(cvFitsLts) <- c("improved", "initial")
fits(cvFitsLts)
fits(cvFitsLts) <- 1:2
cvFitsLts