# NOT RUN {
# Compute the adjusted outlyingness of a simple
# two-dimensional dataset. Outliers are plotted
# in red.
data("geological")
BivData <- geological[c("MnO","MgO")]
Result <- adjOutl(x = BivData)
IndOutliers <- which(!Result$flagX)
plot(BivData)
points(BivData[IndOutliers,], col = "red")
# The number of directions may be specified through
# the option list. The resulting adjusted outlyingness
# is monotone increasing in the number of directions.
Result1 <- adjOutl(x = BivData,
options = list(ndir = 50)
)
Result2 <- adjOutl(x = BivData,
options = list(ndir = 100)
)
which(Result2$outlyingnessX - Result1$outlyingnessX < 0)
# This is however not the case when the seed is changed
Result1 <- adjOutl(x = BivData,
options = list(ndir = 50)
)
Result2 <- adjOutl(x = BivData,
options = list(ndir = 100,
seed = 950)
)
plot(Result2$outlyingnessX - Result1$outlyingnessX,
xlab = "Index", ylab = "Difference in AO")
# We can also consider directions through two data
# points. If the sample is small enough one may opt
# to search over all choose(n,2) directions.
# Note that the computational load increases dramatically
# as n becomes larger.
data("bloodfat")
BivData <- bloodfat[1:100,] # Consider a small toy example.
Result <- adjOutl(x = BivData,
options = list(type = "Rotation",
ndir = "all")
)
IndOutliers <- which(!Result$flagX)
plot(BivData)
points(BivData[IndOutliers,], col = "red")
# Alternatively one may consider randomly generated directions.
data("bloodfat")
Result = adjOutl(x = bloodfat,
options = list(type = "Shift",
ndir = 1000)
)
IndOutliers <- which(!Result$flagX)
plot(bloodfat)
points(bloodfat[IndOutliers,], col = "red")
# }
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