plot_outliers {nexus}

Plot Outliers

Description

Plot Outliers

Usage

## S4 method for signature 'OutlierIndex,missing'
plot(
  x,
  ...,
  qq = FALSE,
  probs = c(0.25, 0.75),
  ncol = NULL,
  flip = FALSE,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  sub = NULL,
  ann = graphics::par("ann"),
  axes = TRUE,
  frame.plot = axes,
  panel.first = NULL,
  panel.last = NULL
)

Arguments

x

An OutlierIndex object.

...

Further graphical parameters.

qq

A logical scalar: should a quantile-quantile plot be produced?

probs

A length-two numeric vector representing probabilities. Corresponding quantile pairs define the line drawn (see stats::qqline()). Only used if qq is TRUE.

ncol

An integer specifying the number of columns to use when facet is "multiple". Defaults to 1 for up to 4 series, otherwise to 2.

flip

A logical scalar: should the y-axis (ticks and numbering) be flipped from side 2 (left) to 4 (right) from group to group?

xlab, ylab

A character vector giving the x and y axis labels.

main

A character string giving a main title for the plot.

sub

A character string giving a subtitle for the plot.

ann

A logical scalar: should the default annotation (title and x and y axis labels) appear on the plot?

axes

A logical scalar: should axes be drawn on the plot?

frame.plot

A logical scalar: should a box be drawn around the plot?

panel.first

An an expression to be evaluated after the plot axes are set up but before any plotting takes place. This can be useful for drawing background grids.

panel.last

An expression to be evaluated after plotting has taken place but before the axes, title and box are added.

Value

plot() is called for its side-effects: is results in a graphic being displayed (invisibly return x).

Author(s)

N. Frerebeau

References

Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection in exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013.

Filzmoser, P. & Hron, K. (2008). Outlier Detection for Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5.

Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation of multivariate outliers for compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014.

See Also

Other outlier detection methods: outliers()

Examples

## Data from Day et al. 2011
data("kommos", package = "folio") # Coerce to compositional data
kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values
coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping

## Detect outliers
out <- outliers(coda, groups = NULL, robust = FALSE)

plot(out) # Plot
plot(out, qq = TRUE) # Quantile-Quantile plot

## Detect outliers by group
out <- outliers(coda[, 1:15, drop = FALSE])

plot(out, ncol = 2) # Plot
plot(out, qq = TRUE, ncol = 4) # Quantile-Quantile plot