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)
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