accessors: Access or set information on resampling-based prediction...
In perry: Resampling-Based Prediction Error Estimation for Regression Models

Description Usage Arguments Value Author(s) See Also Examples

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.

peNames(x)

peNames(x) <- value

fits(x)

fits(x) <- value

npe(x)

nfits(x)

`x`	an object inheriting from class `"perry"` or `"perrySelect"` that contains prediction error results.
`value`	a vector of replacement values.

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".

Andreas Alfons

perryFit, perrySelect, perryTuning

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