Description Usage Arguments Value Author(s) See Also Examples

Compute summary statistics of results from repeated
*K*-fold cross-validation.

1 2 3 4 5 6 7 8 9 10 |

`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 cross-validation results for which to compute the summary statistics. |

`...` |
for the |

The `"cv"`

method returns a vector or matrix of
aggregated cross-validation results, depending on whether
`FUN`

returns a single value or a vector.

For the other methods, a data frame containing the
aggregated cross-validation results for each model is
returned. In the case of the `"cvTuning"`

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

Andreas Alfons

`cvFit`

, `cvSelect`

,
`cvTuning`

, `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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | ```
library("robustbase")
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
fitLts50 <- ltsReg(Y ~ ., data = coleman, alpha = 0.5)
cvFitLts50 <- cvLts(fitLts50, cost = rtmspe, folds = folds,
fit = "both", trim = 0.1)
# 75% subsets
fitLts75 <- ltsReg(Y ~ ., data = coleman, alpha = 0.75)
cvFitLts75 <- cvLts(fitLts75, cost = rtmspe, folds = folds,
fit = "both", trim = 0.1)
# combine results into one object
cvFitsLts <- cvSelect("0.5" = cvFitLts50, "0.75" = cvFitLts75)
cvFitsLts
# summary of the results with the 50% subsets
aggregate(cvFitLts50, summary)
# summary of the combined results
aggregate(cvFitsLts, summary)
## evaluate MM regression models tuned for
## 80%, 85%, 90% and 95% efficiency
tuning <- list(tuning.psi=c(3.14, 3.44, 3.88, 4.68))
# set up function call
call <- call("lmrob", formula = Y ~ .)
# perform cross-validation
cvFitsLmrob <- cvTuning(call, data = coleman,
y = coleman$Y, tuning = tuning, cost = rtmspe,
folds = folds, costArgs = list(trim = 0.1))
cvFitsLmrob
# summary of results
aggregate(cvFitsLmrob, summary)
``` |

```
Loading required package: lattice
Loading required package: robustbase
5-fold CV results:
Fit reweighted raw
1 0.5 1.140772 1.511817
2 0.75 0.963192 1.165930
Best model:
reweighted raw
"0.75" "0.75"
reweighted raw
Min. 0.8426695 1.121681
1st Qu. 0.9832825 1.476071
Median 1.1400160 1.502916
Mean 1.1407723 1.511817
3rd Qu. 1.2835384 1.623199
Max. 1.5522526 1.875849
Fit reweighted.Min. reweighted.1st Qu. reweighted.Median reweighted.Mean
1 0.5 0.8426695 0.9832825 1.1400160 1.1407723
2 0.75 0.8360649 0.8979938 0.9829863 0.9631920
reweighted.3rd Qu. reweighted.Max. raw.Min. raw.1st Qu. raw.Median raw.Mean
1 1.2835384 1.5522526 1.121681 1.476071 1.502916 1.511817
2 1.0241433 1.0850610 1.046983 1.097745 1.145762 1.165930
raw.3rd Qu. raw.Max.
1 1.623199 1.875849
2 1.234328 1.298372
5-fold CV results:
tuning.psi CV
1 3.14 0.9683601
2 3.44 0.9654708
3 3.88 0.9681099
4 4.68 0.9865325
Optimal tuning parameter:
tuning.psi
CV 3.44
tuning.psi CV.Min. CV.1st Qu. CV.Median CV.Mean CV.3rd Qu. CV.Max.
1 3.14 0.8387515 0.9198769 0.9743848 0.9683601 1.0278803 1.0829762
2 3.44 0.8375457 0.9203998 0.9701659 0.9654708 1.0197471 1.0855025
3 3.88 0.8375048 0.9220203 0.9713238 0.9681099 1.0395408 1.0982037
4 4.68 0.8087384 0.9208760 0.9892362 0.9865325 1.0601024 1.1479685
```

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