plot.imp: Plot method for objects of class imp

In robCompositions: Compositional Data Analysis

Plot method for objects of class imp

Description

This function provides several diagnostic plots for the imputed data set in order to see how the imputated values are distributed in comparison with the original data values.

Usage

## S3 method for class 'imp'
plot(
  x,
  ...,
  which = 1,
  ord = 1:ncol(x),
  colcomb = "missnonmiss",
  plotvars = NULL,
  col = c("skyblue", "red"),
  alpha = NULL,
  lty = par("lty"),
  xaxt = "s",
  xaxlabels = NULL,
  las = 3,
  interactive = TRUE,
  pch = c(1, 3),
  ask = prod(par("mfcol")) < length(which) && dev.interactive(),
  center = FALSE,
  scale = FALSE,
  id = FALSE,
  seg.l = 0.02,
  seg1 = TRUE
)

Arguments

x	object of class ‘imp’
...	other parameters to be passed through to plotting functions.
which	if a subset of the plots is required, specify a subset of the numbers 1:3.
ord	determines the ordering of the variables
colcomb	if colcomb=“missnonmiss”, observations with missings in any variable are highlighted. Otherwise, observations with missings in any of the variables specified by colcomb are highlighted in the parallel coordinate plot.
plotvars	Parameter for the parallel coordinate plot. A vector giving the variables to be plotted. If NULL (the default), all variables are plotted.
col	a vector of length two giving the colors to be used in the plot. The second color will be used for highlighting.
alpha	a numeric value between 0 and 1 giving the level of transparency of the colors, or NULL. This can be used to prevent overplotting.
lty	a vector of length two giving the line types. The second line type will be used for the highlighted observations. If a single value is supplied, it will be used for both non-highlighted and highlighted observations.
xaxt	the x-axis type (see par).
xaxlabels	a character vector containing the labels for the x-axis. If NULL, the column names of x will be used.
las	the style of axis labels (see par).
interactive	a logical indicating whether the variables to be used for highlighting can be selected interactively (see ‘Details’).
pch	a vector of length two giving the symbol of the plotting points. The symbol will be used for the highlighted observations. If a single value is supplied, it will be used for both non-highlighted and highlighted observations.
ask	logical; if TRUE, the user is asked before each plot, see par(ask=.).
center	logical, indicates if the data should be centered prior plotting the ternary plot.
scale	logical, indicates if the data should be centered prior plotting the ternary plot.
id	reads the position of the graphics pointer when the (first) mouse button is pressed and returns the corresponding index of the observation. (only used by the ternary plot)
seg.l	length of the plotting symbol (spikes) for the ternary plot.
seg1	if TRUE, the spikes of the plotting symbol are justified.

Details

The first plot (which == 1) is a multiple scatterplot where for the imputed values another plot symbol and color is used in order to highlight them. Currently, the ggpairs functions from the GGally package is used.

Plot 2 is a parallel coordinate plot in which imputed values in certain variables are highlighted. In parallel coordinate plots, the variables are represented by parallel axes. Each observation of the scaled data is shown as a line. If interactive is TRUE, the variables to be used for highlighting can be selected interactively. Observations which includes imputed values in any of the selected variables will be highlighted. A variable can be added to the selection by clicking on a coordinate axis. If a variable is already selected, clicking on its coordinate axis will remove it from the selection. Clicking anywhere outside the plot region quits the interactive session.

Plot 3 shows a ternary diagram in which imputed values are highlighted, i.e. those spikes of the chosen plotting symbol are colored in red for which of the values are missing in the unimputed data set.

Value

None (invisible NULL).

Author(s)

Matthias Templ

References

Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman and Hall Ltd., London (UK). 416p.

Wegman, E. J. (1990) Hyperdimensional data analysis using parallel coordinates Journal of the American Statistical Association 85, 664–675.

See Also

impCoda, impKNNa

Examples

data(expenditures)
expenditures[1,3]
expenditures[1,3] <- NA
xi <- impKNNa(expenditures)
xi
summary(xi)
## Not run: plot(xi, which=1)
plot(xi, which=2)
plot(xi, which=3)
plot(xi, which=3, seg1=FALSE)

robCompositions documentation built on Aug. 25, 2023, 5:13 p.m.