perryReshape: Reshape resampling-based prediction error results
In perry: Resampling-Based Prediction Error Estimation for Regression Models

Description Usage Arguments Value Author(s) References See Also Examples

Reshape resampling-based prediction error results into an object of class "perrySelect" with only one column of results.

perryReshape(
  x,
  selectBest = c("min", "hastie"),
  seFactor = 1,
  tuning = list(),
  ...
)

`x`	an object inheriting from class `"perry"` or `"perrySelect"` that contains prediction error 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 nested models or for models with a tuning parameter controlling the complexity of the model (e.g., penalized regression). It selects the most parsimonious model whose prediction error is no larger than `seFactor` standard errors above the prediction error of the best overall model. Note that the models are thereby assumed to be ordered from the most parsimonious one to the most complex one. In particular a one-standard-error rule is frequently applied.
`seFactor`	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"`.
`tuning`	a list of tuning parameter values that correspond to the different prediction error results. The names of the list components should thereby correspond to the argument names of the tuning parameters. For each tuning parameter, a vector of values can be supplied. A data frame containing all possible combinations of tuning parameter values is then added to the reshaped prediction error results.
`...`	additional arguments to be passed down.

An object of class "perrySelect" (subclass "perryTuning" if a list of tuning parameters is supplied) with the following components:

pe: a data frame containing the estimated prediction errors for the models. In case of more than one resampling replication, those are average values over all replications.
se: a data frame containing the estimated standard errors of the prediction loss for the models.
reps: a data frame containing the estimated prediction errors for the models from all replications. This is only returned in case of more than one resampling replication.
splits: an object giving the data splits used to estimate the prediction error.
y: the response.
yHat: a list containing the predicted values for the models. Each list component is again a list containing the corresponding predicted values from all replications.
best: an integer giving the index of the model with the best prediction performance.
selectBest: a character string specifying the criterion used for selecting the best model.
seFactor: a numeric value giving the multiplication factor of the standard error used for the selection of the best model.
tuning: a data frame containing the grid of tuning parameter values that correspond to the different prediction error results (only subclass "perryTuning").

Andreas Alfons

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

perryFit, perrySelect, perryTuning

library("perryExamples")
data("coleman")

# perform cross-validation for an LTS regression model
fit <- ltsReg(Y ~ ., data = coleman)
folds <- foldControl(K = 5, R = 10)
cv <- perry(fit, splits = folds, fit = "both",
            cost = rtmspe, trim = 0.1, seed = 1234)

# compare original and reshaped object
cv
perryReshape(cv)