Plot Outliers

Plot Outliers

Usage

# S4 method for 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).

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 .

Author

N. Frerebeau

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
#> 1 qualitative variable was removed: date.

## 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