accessors {perry}

Access or set information on resampling-based prediction error results

Description

Retrieve or set the names of resampling-based prediction error results, retrieve or set the identifiers of the models, or retrieve the number of prediction error results or included models.

Usage

peNames(x)

peNames(x) <- value

fits(x)

fits(x) <- value

npe(x)

nfits(x)

Arguments

x

an object inheriting from class "perry" or "perrySelect" that contains prediction error results.

value

a vector of replacement values.

Value

peNames returns the names of the prediction error results. The replacement function thereby returns them invisibly.

fits returns the identifiers of the models for objects inheriting from class "perrySelect" and NULL for objects inheriting from class "perry". The replacement function thereby returns those values invisibly.

npe returns the number of prediction error results.

nfits returns the number of models included in objects inheriting from class "perrySelect" and NULL for objects inheriting from class "perry".

Author(s)

Andreas Alfons

See Also

perryFit, perrySelect, perryTuning

Examples

library("perryExamples")
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
fit50 <- ltsReg(Y ~ ., data = coleman, alpha = 0.5)
cv50 <- perry(fit50, splits = folds, fit = "both",
              cost = rtmspe, trim = 0.1)

# 75% subsets
fit75 <- ltsReg(Y ~ ., data = coleman, alpha = 0.75)
cv75 <- perry(fit75, splits = folds, fit = "both",
              cost = rtmspe, trim = 0.1)

# combine results into one object
cv <- perrySelect("0.5" = cv50, "0.75" = cv75)
cv

# "perry" object
npe(cv50)
nfits(cv50)
peNames(cv50)
peNames(cv50) <- c("improved", "initial")
fits(cv50)
cv50

# "perrySelect" object
npe(cv)
nfits(cv)
peNames(cv)
peNames(cv) <- c("improved", "initial")
fits(cv)
fits(cv) <- 1:2
cv