Reshape resampling-based prediction error results into an object of class
"perrySelect" with only one column of results.
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an object inheriting from class
a character string specifying a criterion for selecting
the best model. Possible values are
a numeric value giving a multiplication factor of the
standard error for the selection of the best model. This is ignored if
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
"perryTuning" if a list of tuning parameters is supplied) with the
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.
a data frame containing the estimated standard errors of the prediction loss for the models.
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.
an object giving the data splits used to estimate the prediction error.
a list containing the predicted values for the models. Each list component is again a list containing the corresponding predicted values from all replications.
an integer giving the index of the model with the best prediction performance.
a character string specifying the criterion used for selecting the best model.
a numeric value giving the multiplication factor of the standard error used for the selection of the best model.
a data frame containing the grid of tuning parameter
values that correspond to the different prediction error results (only
Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd edition.
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