perry (version 0.3.1)

subset.perry: Subsetting resampling-based prediction error results

Description

Extract subsets of resampling-based prediction error results.

Usage

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

See Also

perryFit, perrySelect, perryTuning, subset

Examples

# NOT RUN {
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")
# }