Nothing
library(fsdaR)
## Stars data from 'robustbase'
data(starsCYG)
head(starsCYG)
## Compute OLS, MM and LTS regression using robustbase and draw
## a scatterplot with the three lines
##
(stars.lm <- lm(log.light~log.Te, data=starsCYG))
(stars.mm <- lmrob(log.light~log.Te, data=starsCYG))
(stars.lts <- ltsReg(log.light~log.Te, data=starsCYG))
## Plot the data with OLS, LTS and MM regression lines
plot(log.light~log.Te, data=starsCYG)
abline(stars.lm, col="blue", lty="solid")
abline(stars.mm, col="red", lty="dashed")
abline(stars.lts, col="green", lty="dashed")
legend("topright", leg=c("MM-est", "OLS", "LTS"), lty=c("dashed", "solid", "dashed"), col=c("red", "blue", "green"))
## LTS-estimation
out <- fsreg(log.light~log.Te, data=starsCYG, method="LTS")
print(out)
summary(out)
## S-estimation monitoring: this will take some time
(out <- fsreg(log.light~log.Te, data=starsCYG, method="S", monitoring=TRUE))
## resfwdplot with no optional paramters
resfwdplot(out)
## resfwdplot with optional paramters: change the color and line type of foreground units
resfwdplot(out, fg.col="red", fg.lty="dotdash")
## resfwdplot with brushing. Note that we are changing the X- and Y-labels of the scatterplot
resfwdplot(out, fg.col="red", fg.lty="dotdash", databrush=TRUE, nameX="log.TE", namey="log.light")
## We get the following picture with MM-estimates, Tukeys bisquare
## function. The three sets of residuals are clearly seen for virtually all efficiency values.
(out <- fsreg(log.light~log.Te, data=starsCYG, method="MM", monitoring=TRUE))
resfwdplot(out, fg.col="red", fg.lty="dotdash")
## Forward search: Stars data. Forward plot of minimum deletion residuals,
## leading to identification of multiple outliers. The given levels of
## the envelopes are for pointwise tests of outlyingness.
##
(out <- fsreg(log.light~log.Te, data=starsCYG, method="FS", monitoring=TRUE))
mdrplot(out)
levfwdplot(out)
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