if (FALSE) {
data(hbk, package="robustbase")
(out <- fsmult(hbk[,1:3]))
class(out)
summary(out)
## Generate contaminated data (200,3)
n <- 200
p <- 3
set.seed(123456)
X <- matrix(rnorm(n*p), nrow=n)
Xcont <- X
Xcont[1:5, ] <- Xcont[1:5,] + 3
out1 <- fsmult(Xcont, trace=TRUE) # no plots (plot defaults to FALSE)
names(out1)
(out1 <- fsmult(Xcont, trace=TRUE, plot=TRUE)) # identical to plot=1
## plot=1 - minimum MD with envelopes based on n observations
## and the scatterplot matrix with the outliers highlighted
(out1 <- fsmult(Xcont, trace=TRUE, plot=1))
## plot=2 - additional plots of envelope resuperimposition
(out1 <- fsmult(Xcont, trace=TRUE, plot=2))
## plots is a list: plots showing envelope superimposition in normal coordinates.
(out1 <- fsmult(Xcont, trace=TRUE, plot=list(ncoord=1)))
## Choosing an initial subset formed by the three observations with
## the smallest Mahalanobis Distance.
(out1 <- fsmult(Xcont, m0=5, crit="md", trace=TRUE))
## fsmult() with monitoring
(out2 <- fsmult(Xcont, monitoring=TRUE, trace=TRUE))
names(out2)
## Monitor the exceedances from m=200 without showing plots.
n <- 1000
p <- 10
Y <- matrix(rnorm(10000), ncol=10)
(out <- fsmult(Y, init=200))
## Forgery Swiss banknotes examples.
data(swissbanknotes)
## Monitor the exceedances of Minimum Mahalanobis Distance
(out1 <- fsmult(swissbanknotes[101:200,], plot=1))
## Control minimum and maximum on the x axis
(out1 <- fsmult(swissbanknotes[101:200,], plot=list(xlim=c(60,90))))
## Monitor the exceedances of Minimum Mahalanobis Distance using
## normal coordinates for mmd.
(out1 <- fsmult(swissbanknotes[101:200,], plot=list(ncoord=1)))
}
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