Description Usage Arguments Details Value Note Author(s) References See Also Examples
Combine resamplingbased prediction error results for various models into one object and select the model with the best prediction performance.
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... 
objects inheriting from class 
.list 
a list of objects inheriting from class 
.reshape 
a logical indicating whether objects with more than one column of prediction error results should be reshaped to have only one column (see “Details”). 
.selectBest 
a character string specifying a criterion for selecting
the best model. Possible values are 
.seFactor 
a numeric value giving a multiplication factor of the
standard error for the selection of the best model. This is ignored if

Keep in mind that objects inheriting from class "perry"
or
"perrySelect"
may contain multiple columns of prediction error
results. This is the case if the response is univariate but the
function to compute predictions (usually the predict
method of the fitted model) returns a matrix.
The .reshape
argument determines how to handle such objects. If
.reshape
is FALSE
, all objects are required to have the same
number of columns and the best model for each column is selected. A typical
use case for this behavior would be if the investigated models contain
prediction error results for a raw and a reweighted fit. It might then be
of interest to researchers to compare the best model for the raw estimators
with the best model for the reweighted estimators.
If .reshape
is TRUE
, objects with more than one column of
results are first transformed with perryReshape
to have only
one column. Then the best overall model is selected.
It should also be noted that the argument names of .list
,
.reshape
, .selectBest
and .seFacor
start with a dot to
avoid conflicts with the argument names used for the objects containing
prediction error results.
An object of class "perrySelect"
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 of the models.
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 vector giving the indices of the models 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.
To ensure comparability, the prediction errors for all models are required to be computed from the same data splits.
Andreas Alfons
Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd edition.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  library("perryExamples")
data("coleman")
set.seed(1234) # set seed for reproducibility
## set up folds for crossvalidation
folds < cvFolds(nrow(coleman), K = 5, R = 10)
## compare LS, MM and LTS regression
# perform crossvalidation for an LS regression model
fitLm < lm(Y ~ ., data = coleman)
cvLm < perry(fitLm, splits = folds,
cost = rtmspe, trim = 0.1)
# perform crossvalidation for an MM regression model
fitLmrob < lmrob(Y ~ ., data = coleman)
cvLmrob < perry(fitLmrob, splits = folds,
cost = rtmspe, trim = 0.1)
# perform crossvalidation for an LTS regression model
fitLts < ltsReg(Y ~ ., data = coleman)
cvLts < perry(fitLts, splits = folds,
cost = rtmspe, trim = 0.1)
# compare crossvalidation results
perrySelect(LS = cvLm, MM = cvLmrob, LTS = cvLts)

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