Nothing
## carbikeplot {fsdaR} -- Produces the carbike plot to find best relevant clustering solutions obtained by tclustICsol
data(geyser2)
out <- tclustIC(geyser2, whichIC="MIXMIX", plot=FALSE, alpha=0.1)
## Plot first two best solutions using as Information criterion MIXMIX
print("Best solutions using MIXMIX")
outMIXMIX <- tclustICsol(out, whichIC="MIXMIX", plot=TRUE, NumberOfBestSolutions=2)
print(outMIXMIX$MIXMIXbs)
carbikeplot(outMIXMIX)
## corfwdplot {fsdaR} -- Monitoring the correlations between consecutive distances or residuals
data(hbk, package="robustbase")
(out <- fsmult(hbk[,1:3], monitoring=TRUE))
corfwdplot(out)
## fsdalms.object {fsdaR} -- Description of fsdalms Objects
## fsdalts.object {fsdaR} -- Description of fsdalts Objects
## fsmeda.object {fsdaR} -- Description of fsmeda Objects
## fsmmmdrs {fsdaR} -- Performs random start monitoring of minimum Mahalanobis distance
## fsmmmdrs.object {fsdaR} -- Description of fsmmmdrs.object Objects
## fsmult {fsdaR} -- Gives an automatic outlier detection procedure in multivariate analysis
## fsmult.object {fsdaR} -- Description of fsmult.object Objects
## fsr.object {fsdaR} -- Description of fsr Objects
## fsreda.object {fsdaR} -- Description of fsreda Objects
## FSReda_control {fsdaR} -- Creates an FSReda_control object
## fsreg {fsdaR} -- fsreg: an automatic outlier detection procedure in linear regression
## FSR_control {fsdaR} -- Creates an FSR_control object
## levfwdplot {fsdaR} -- Plots the trajectories of the monitored scaled (squared) residuals
n <- 100
y <- rnorm(n)
X <- matrix(rnorm(n*4), nrow=n)
out <- fsreg(y~X, method="LTS")
out <- fsreg(y~X, method="FS", bsb=out$bs, monitoring=TRUE)
levfwdplot(out)
## LXS_control {fsdaR} -- Creates an LSX_control object
## malfwdplot {fsdaR} -- Plots the trajectories of scaled Mahalanobis distances along the search
## Produce monitoring MD plot with all the default options.
## Generate input structure for malfwdplot
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, fg.cex=0)
## malindexplot {fsdaR} -- Plots the trajectory of minimum Mahalanobis distance (mmd)
## Mahalanobis distance plot of 100 random numbers.
## Numbers are from from the chisq with 5 degrees of freedom
malindexplot(rchisq(100, 5), 5)
## mdrplot {fsdaR} -- Plots the trajectory of minimum deletion residual (mdr)
n <- 100
y <- rnorm(n)
X <- matrix(rnorm(n*4), nrow=n)
out <- fsreg(y~X, method="LTS")
out <- fsreg(y~X, method="FS", bsb=out$bs, monitoring=TRUE)
mdrplot(out)
## mmdplot {fsdaR} -- Plots the trajectory of minimum Mahalanobis distance (mmd)
data(hbk)
(out <- fsmult(hbk[,1:3], monitoring=TRUE))
mmdplot(out)
## mmdrsplot {fsdaR} -- Plots the trajectories of minimum Mahalanobis distances from different starting points
data(hbk)
out <- fsmmmdrs(hbk[,1:3])
mmdrsplot(out)
## mmmult {fsdaR} -- Computes MM estimators in multivariate analysis with auxiliary S-scale
data(hbk)
(out <- mmmult(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 <- mmmult(Xcont, trace=TRUE) # no plots (plot defaults to FALSE)
names(out1)
## plot=TRUE - generates: (1) a plot of Mahalanobis distances against
## index number. The confidence level used to draw the confidence bands for
## the MD is given by the input option conflev. If conflev is
## not specified a nominal 0.975 confidence interval will be used and
## (2) a scatter plot matrix with the outliers highlighted.
(out1 <- mmmult(Xcont, trace=TRUE, plot=TRUE))
## plots is a list: the spm shows the labels of the outliers.
(out1 <- mmmult(Xcont, trace=TRUE, plot=list(labeladd="1")))
## plots is a list: the spm uses the variable names provided by 'nameY'.
(out1 <- mmmult(Xcont, trace=TRUE, plot=list(nameY=c("A", "B", "C"))))
## mmmult() with monitoring
(out2 <- mmmult(Xcont, monitoring=TRUE, trace=TRUE))
names(out2)
## Forgery Swiss banknotes examples.
data(swissbanknotes)
(out1 <- mmmult(swissbanknotes[101:200,], plot=TRUE))
(out1 <- mmmult(swissbanknotes[101:200,], plot=list(labeladd="1")))
## mmmult.object {fsdaR} -- Description of mmmult.object Objects
data(hbk)
(out <- mmmult(hbk[,1:3]))
class(out)
summary(out)
##---------------------------------------------------------------------------
##
## DATA SETS
##
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