cvTools (version 0.2.0)

cvReshape: Reshape cross-validation results

Description

Reshape cross-validation results into an object of class "cvSelect" with only one column of results.

Usage

cvReshape(x, ...)
  ## S3 method for class 'cv':
cvReshape(x,
    selectBest = c("min", "hastie"), sdFactor = 1, ...)
  ## S3 method for class 'cvSelect':
cvReshape(x,
    selectBest = c("min", "hastie"), sdFactor = 1, ...)

Arguments

x

an object inheriting from class "cv" or "cvSelect" that contains cross-validation results.

selectBest

a character string specifying a criterion for selecting the best model. Possible values are "min" (the default) or "hastie". The former selects the model with the smallest prediction error. The latter is useful for nes

sdFactor

a numeric value giving a multiplication factor of the standard error for the selection of the best model. This is ignored if selectBest is "min".

...

additional arguments to be passed down.

Value

• An object of class "cvSelect" with the following components:
• n
  an integer giving the number of observations.
• K
  an integer giving the number of folds used in cross-validation.
• R
  an integer giving the number of replications used in cross-validation.
• best
  an integer giving the index of the model with the best prediction performance.
• cv
  a data frame containing the estimated prediction errors for the models. For repeated cross-validation, those are average values over all replications.
• sd
  a data frame containing the estimated standard errors of the prediction loss for the models.
• selectBest
  a character string specifying the criterion used for selecting the best model.
• sdFactor
  a numeric value giving the multiplication factor of the standard error used for the selection of the best model.
• reps
  a data frame containing the estimated prediction errors for the models from all replications. This is only returned if repeated cross-validation was performed.

References

Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd edition.

See Also

cvFit, cvSelect, cvTuning

Examples

library("robustbase")
data("coleman")

# perform cross-validation for an LTS regression model
fitLts <- ltsReg(Y ~ ., data = coleman)
cvFitLts <- cvLts(fitLts, cost = rtmspe, K = 5, R = 10, 
    fit = "both", trim = 0.1, seed = 1234)
# compare original and reshaped object
cvFitLts
cvReshape(cvFitLts)