summary.perry: Summarize resampling-based prediction error results

In perry: Resampling-Based Prediction Error Estimation for Regression Models

Description

Produce a summary of resampling-based prediction error results.

Usage

## S3 method for class 'perry'
summary(object, ...)

## S3 method for class 'perrySelect'
summary(object, ...)

## S3 method for class 'perryTuning'
summary(object, ...)

Arguments

object	an object inheriting from class "perry" or "perrySelect" that contains prediction error results (note that the latter includes objects of class "perryTuning").
...	currently ignored.

Value

An object of class "summary.perry", "summary.perrySelect" or "summary.perryTuning", depending on the class of object.

Author(s)

Andreas Alfons

See Also

perryFit, perrySelect, perryTuning, summary

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

# summary of the results with the 50% subsets
summary(cv50)
# summary of the combined results
summary(cv)

perry documentation built on Nov. 3, 2021, 5:08 p.m.