R: Subsetting cross-validation results

subset.cv {cvTools} | R Documentation

## Subsetting cross-validation results

### Description

Extract subsets of results from (repeated) K-fold cross-validation.

### Usage

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

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

### Arguments

`x` | an object inheriting from class `"cv"` or `"cvSelect"` that contains cross-validation results.
`subset` | a character, integer or logical vector indicating the subset of models for which to keep the cross-validation results.
`select` | a character, integer or logical vector indicating the columns of cross-validation results to be extracted.
`...` | currently ignored.

### Value

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

### Author(s)

Andreas Alfons

### See Also

`cvFit`, `cvSelect`, `cvTuning`, `subset`

### Examples

    library("robustbase")
    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
    fitLts50 <- ltsReg(Y ~ ., data = coleman, alpha = 0.5)
    cvFitLts50 <- cvLts(fitLts50, cost = rtmspe, folds = folds,
        fit = "both", trim = 0.1)

    # 75% subsets
    fitLts75 <- ltsReg(Y ~ ., data = coleman, alpha = 0.75)
    cvFitLts75 <- cvLts(fitLts75, cost = rtmspe, folds = folds,
        fit = "both", trim = 0.1)

    # combine results into one object
    cvFitsLts <- cvSelect("0.5" = cvFitLts50, "0.75" = cvFitLts75)
    cvFitsLts

    # extract reweighted LTS results with 50% subsets
    subset(cvFitLts50, select = "reweighted")
    subset(cvFitsLts, subset = c(TRUE, FALSE), select = "reweighted")

[Package cvTools version 0.3.2 Index]