test-fsmult.R
In fsdaR: Robust Data Analysis Through Monitoring and Dynamic Visualization

library(fsdaR)

n <- 200
v <- 3
set.seed(123456)
X <- matrix(rnorm(n*v), nrow=n)

Xcont <- X
Xcont[1:5, ] <- Xcont[1:5, ] + 3

## --------------------------------------------------------------
## Testing fsmult()
(out1 <- fsmult(Xcont, trace=TRUE))               # no plots (plot defaults to FALSE)
names(out1)

(out1 <- fsmult(Xcont, trace=TRUE, plot=TRUE))    # identical to plots=1
(out1 <- fsmult(Xcont, trace=TRUE, plot=1))       # plots=1 - minimum MD with envelopes based on n observations and the scatterplot matrix with the outliers highlighted
(out1 <- fsmult(Xcont, trace=TRUE, plot=2))       # plots=2 - additional plots of envelope resuperimposition

## 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.
set.seed(123456)
n <- 1000
p <- 10
X <- matrix(rnorm(10000), ncol=10)
(out <- fsmult(X, 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,], plots=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)))

malfwdplot(out2)
mmdplot(out2)
malindexplot(out1)
malindexplot(rnorm(100), p=5)

#################################################################
## Produce monitoring MD plot with all the default options.
##  Generate input structure for malfwdplot
set.seed(123456)
n <- 100
p <- 4
Y <- matrix(rnorm(n*p), ncol=p)
Y[1:10,] <- Y[1:10,] + 4

(out <- fsmult(Y, monitoring=TRUE, init=30))

##  Produce monitoring MD plot with all the default options
malfwdplot(out)

#################################################################
##
##  Mahalanobis distance plot of 100 random numbers.
##  Numbers are from from the chisq with 5 degrees of freedom
##
set.seed(12345)
malindexplot(rchisq(100, 5), 5)