subset.perry {perry}

Subsetting resampling-based prediction error results

Description

Extract subsets of resampling-based prediction error results.

Usage

## S3 method for class 'perry'
subset(x, select = NULL, ...)

## S3 method for class 'perrySelect'
subset(x, subset = NULL, select = NULL, ...)

Arguments

x

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

select

a character, integer or logical vector indicating the prediction error results to be extracted.

...

currently ignored.

subset

a character, integer or logical vector indicating the subset of models for which to keep the prediction error results.

Value

An object similar to x containing just the selected results.

Note

Duplicate indices in subset or select are removed such that all models and prediction error results are unique.

Author(s)

Andreas Alfons

See Also

perryFit, perrySelect, perryTuning, subset

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

# extract reweighted LTS results with 50% subsets
subset(cv50, select = "reweighted")
subset(cv, subset = c(TRUE, FALSE), select = "reweighted")