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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