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

Compute summary statistics of resampling-based prediction error results.

1 2 3 4 5 6 7 8 |

`x` |
an object inheriting from class |

`FUN` |
a function to compute the summary statistics. |

`select` |
a character, integer or logical vector indicating the columns of prediction error results for which to compute the summary statistics. |

`...` |
for the |

The `"perry"`

method returns a vector or matrix of aggregated
prediction error results, depending on whether `FUN`

returns a single
value or a vector.

For the other methods, a data frame containing the aggregated
prediction error results for each model is returned. In the case of the
`"perryTuning"`

method, the data frame contains the combinations of
tuning parameters rather than a column describing the models.

Duplicate indices in `subset`

or `select`

are removed such
that all models and prediction error results are unique.

Andreas Alfons

`perryFit`

, `perrySelect`

,
`perryTuning`

, `aggregate`

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 27 28 | ```
library("perryExamples")
data("coleman")
set.seed(1234) # set seed for reproducibility
## set up folds for cross-validation
folds <- cvFolds(nrow(coleman), K = 5, R = 10)
## compare raw and reweighted LTS estimators for
## 50% and 75% subsets
# 50% subsets
fit50 <- ltsReg(Y ~ ., data = coleman, alpha = 0.5)
cv50 <- perry(fit50, splits = folds, fit = "both",
cost = rtmspe, trim = 0.1)
# 75% subsets
fit75 <- ltsReg(Y ~ ., data = coleman, alpha = 0.75)
cv75 <- perry(fit75, splits = folds, fit = "both",
cost = rtmspe, trim = 0.1)
# combine results into one object
cv <- perrySelect("0.5" = cv50, "0.75" = cv75)
cv
# summary of the results with the 50% subsets
aggregate(cv50, summary)
# summary of the combined results
aggregate(cv, summary)
``` |

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