perry (version 0.1.1)

subset.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.

subset

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

select

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

...

currently ignored.

Value

• An object similar to x containing just the selected results.

See Also

perryFit, perrySelect, perryTuning, subset

Examples

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 <- repCV(fit50, folds = folds, fit = "both", 
    cost = rtmspe, trim = 0.1)

# 75\% subsets
fit75 <- ltsReg(Y ~ ., data = coleman, alpha = 0.75)
cv75 <- repCV(fit75, folds = 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")