R: Summarize cross-validation results

summary.cv {cvTools} | R Documentation

## Summarize cross-validation results

### Description

Produce a summary of results from (repeated) K-fold cross-validation.

### Usage

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

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

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

### Arguments

`object` | an object inheriting from class `"cv"` or `"cvSelect"` that contains cross-validation results (note that the latter includes objects of class `"cvTuning"`).
`...` | currently ignored.

### Value

An object of class `"summary.cv"`, `"summary.cvSelect"` or `"summary.cvTuning"`, depending on the class of `object`.

### Author(s)

Andreas Alfons

### See Also

`cvFit`, `cvSelect`, `cvTuning`, `summary`

### 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

    # summary of the results with the 50% subsets
    summary(cvFitLts50)
    # summary of the combined results
    summary(cvFitsLts)

    ## evaluate MM regression models tuned for
    ## 80%, 85%, 90% and 95% efficiency
    tuning <- list(tuning.psi=c(3.14, 3.44, 3.88, 4.68))

    # set up function call
    call <- call("lmrob", formula = Y ~ .)
    # perform cross-validation
    cvFitsLmrob <- cvTuning(call, data = coleman,
        y = coleman$Y, tuning = tuning, cost = rtmspe,
        folds = folds, costArgs = list(trim = 0.1))
    cvFitsLmrob

    # summary of results
    summary(cvFitsLmrob)

[Package cvTools version 0.3.2 Index]