symbol.plot: Symbol Plot

In mvoutlier: Multivariate Outlier Detection Based on Robust Methods

Description

The function symbol.plot plots the (two-dimensional) data using different symbols according to the robust mahalanobis distance based on the mcd estimator with adjustment.

Usage

symbol.plot(x, quan=1/2, alpha=0.025, ...)

Arguments

x	two dimensional matrix or data.frame containing the data.
quan	amount of observations which are used for mcd estimations. has to be between 0.5 and 1, default ist 0.5
alpha	amount of observations used for calculating the adjusted quantile (see function arw).
...	additional graphical parameters

Details

The function symbol.plot plots the (two-dimensional) data using different symbols. In addition a legend and four ellipsoids are drawn, on which mahalanobis distances are constant. As the legend shows, these constant values correspond to the 25%, 50%, 75% and adjusted (see function arw) quantiles of the chi-square distribution.

Value

outliers	boolean vector of outliers
md	robust mahalanobis distances of the data

Author(s)

Moritz Gschwandtner <[e-mail]>
Peter Filzmoser <[e-mail]> http://cstat.tuwien.ac.at/filz/

References

P. Filzmoser, R.G. Garrett, and C. Reimann. Multivariate outlier detection in exploration geochemistry. Computers & Geosciences, 31:579-587, 2005.

See Also

dd.plot, color.plot, arw

Examples

# create data:
x <- cbind(rnorm(100), rnorm(100))
y <- cbind(rnorm(10, 5, 1), rnorm(10, 5, 1))
z <- rbind(x,y)
# execute:
symbol.plot(z, quan=0.75)

Example output

Loading required package: sgeostat
sROC 0.1-2 loaded
$outliers
  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [49] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
 [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
 [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [97] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[109]  TRUE  TRUE

$md
  [1] 1.13024250 2.50336807 1.12622782 0.48623021 1.12912146 0.70746739
  [7] 0.56254555 0.34806481 1.69845854 1.31832092 0.43170915 1.05622027
 [13] 0.77294666 0.52796288 1.03285440 1.01315673 1.18917433 0.74008330
 [19] 1.00147022 1.65276244 0.42150962 0.64762837 0.98113250 1.45352877
 [25] 1.72368674 2.67241992 1.65077946 1.29098118 2.38882515 0.69432449
 [31] 0.82755793 2.94791060 0.99014063 0.73090444 0.59818878 1.11504543
 [37] 0.88490392 1.65904658 0.71956410 1.23030531 1.48112170 0.83128535
 [43] 0.25140668 1.79430649 0.85553563 0.59465146 1.54924258 0.52076842
 [49] 0.29244072 1.52542366 0.22950350 1.04666653 1.29588042 0.70052240
 [55] 2.87264582 2.39004676 0.77658749 1.84812677 1.34215640 0.30215641
 [61] 0.70299880 1.69011099 2.02427067 0.61525974 0.24103173 0.79674405
 [67] 0.89518364 0.97926938 1.69826815 1.08855101 1.48785325 0.54174089
 [73] 1.09908292 1.25183990 0.28964088 1.61561191 0.83880792 0.70448599
 [79] 0.27326605 1.59222711 1.91705475 0.98338860 0.66409108 2.78680331
 [85] 2.40064260 2.11575156 1.34112465 1.05393122 1.46202276 1.14855705
 [91] 1.85558176 0.30936412 1.26960538 0.51086629 0.07413088 0.56519331
 [97] 0.61138536 0.52201934 0.97717568 0.58158145 7.45695974 9.15951757
[103] 5.29719434 6.80888726 8.51177965 6.90920696 5.46316029 6.00925122
[109] 6.95891011 6.68711704

mvoutlier documentation built on July 30, 2021, 9:09 a.m.