perry (version 0.2.0)

perryReshape: Reshape resampling-based prediction error results

Description

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

Usage

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

Arguments

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".

…

additional arguments to be passed down.

Value

An object of class "perrySelect" with the following components:

splits

an object giving the data splits used to estimate the prediction error.

best

an integer giving the index of the model with the best prediction performance.

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.

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.

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.

References

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

See Also

perryFit, perrySelect, perryTuning

Examples

# NOT RUN {
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)
# }