Description Usage Arguments Value Author(s) See Also Examples
Recompute prediction error measures for previously obtained objects that contain resampling-based prediction error results. This is useful for computing a different measure of prediction loss.
1 2 3 4 5 6 7 |
object |
an object inheriting from class |
... |
for the generic function, additional arguments to be passed
down to methods. For the methods, additional arguments to be passed to the
prediction loss function |
cost |
a cost function measuring prediction loss. It should expect
the observed values of the response to be passed as the first argument and
the predicted values as the second argument, and must return either a
non-negative scalar value, or a list with the first component containing
the prediction error and the second component containing the standard
error. The default is to use the root mean squared prediction error
(see |
An object similar to object
containing the results for the
new measure of prediction loss.
Andreas Alfons
perryFit
, perryTuning
,
perrySelect
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 | 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 based on their RTMSPE with 25% trimming
# 50% subsets
fit50 <- ltsReg(Y ~ ., data = coleman, alpha = 0.5)
cv50 <- perry(fit50, splits = folds, fit = "both",
cost = rtmspe, trim = 0.25)
# 75% subsets
fit75 <- ltsReg(Y ~ ., data = coleman, alpha = 0.75)
cv75 <- perry(fit75, splits = folds, fit = "both",
cost = rtmspe, trim = 0.25)
# combine results into one object
cv <- perrySelect("0.5" = cv50, "0.75" = cv75)
cv
## recompute the RTMSPE with 10% trimming
reperry(cv50, cost = rtmspe, trim = 0.1)
reperry(cv, cost = rtmspe, trim = 0.1)
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