Nothing

```
library("robustbase")
data("coleman")
set.seed(1234) # set seed for reproducibility
# set up folds for cross-validation
folds <- cvFolds(nrow(coleman), K = 5, R = 50)
## compare LS, MM and LTS regression
# perform cross-validation for an LS regression model
fitLm <- lm(Y ~ ., data = coleman)
cvFitLm <- cvLm(fitLm, cost = rtmspe,
folds = folds, trim = 0.1)
# perform cross-validation for an MM regression model
fitLmrob <- lmrob(Y ~ ., data = coleman, k.max = 500)
cvFitLmrob <- cvLmrob(fitLmrob, cost = rtmspe,
folds = folds, trim = 0.1)
# perform cross-validation for an LTS regression model
fitLts <- ltsReg(Y ~ ., data = coleman)
cvFitLts <- cvLts(fitLts, cost = rtmspe,
folds = folds, trim = 0.1)
# combine results into one object
cvFits <- cvSelect(LS = cvFitLm, MM = cvFitLmrob, LTS = cvFitLts)
cvFits
# plot results for the MM regression model
plot(cvFitLmrob, method = "bw")
plot(cvFitLmrob, method = "density")
# plot combined results
plot(cvFits, method = "bw")
plot(cvFits, method = "density")
plot(cvFits, method = "xy")
plot(cvFits, method = "dot")
## 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
# plot combined results
plot(cvFitsLts, method = "bw")
plot(cvFitsLts, method = "density")
plot(cvFitsLts, method = "xy")
plot(cvFitsLts, method = "dot")
```

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