plotXYResiduals.pls: Residual XY-distance plot

In mdatools: Multivariate Data Analysis for Chemometrics

Residual XY-distance plot

Description

Shows a plot with full X-distance (f) vs. orthogonal Y-distance (z) for PLS model results.

Usage

## S3 method for class 'pls'
plotXYResiduals(
  obj,
  ncomp = obj$ncomp.selected,
  norm = TRUE,
  log = FALSE,
  main = sprintf("XY-distances (ncomp = %d)", ncomp),
  cgroup = NULL,
  xlim = NULL,
  ylim = NULL,
  show.limits = c(TRUE, TRUE),
  lim.col = c("darkgray", "darkgray"),
  lim.lwd = c(1, 1),
  lim.lty = c(2, 3),
  show.legend = TRUE,
  legend.position = "topright",
  res = obj$res,
  ...
)

Arguments

obj	a PLS model (object of class pls)
ncomp	how many components to use (by default optimal value selected for the model will be used)
norm	logical, normalize distance values or not (see details)
log	logical, apply log tranformation to the distances or not (see details)
main	title for the plot
cgroup	color grouping of plot points (works only if one result object is available)
xlim	limits for x-axis
ylim	limits for y-axis
show.limits	vector with two logical values defining if limits for extreme and/or outliers must be shown
lim.col	vector with two values - line color for extreme and outlier limits
lim.lwd	vector with two values - line width for extreme and outlier limits
lim.lty	vector with two values - line type for extreme and outlier limits
show.legend	logical, show or not a legend on the plot (needed if several result objects are available)
legend.position	position of legend (if shown)
res	list with result objects to show the plot for (by defaul, model results are used)
...	other plot parameters (see mdaplotg for details)

Details

The function presents a way to identify extreme objects and outliers based on both full distance for X-decomposition (known as f) and squared residual distance for Y-decomposition (z). The approach has been proposed in [1].

The plot is available only if data driven methods (classic or robust) have been used for computing of critical limits.

References

1. Rodionova O. Ye., Pomerantsev A. L. Detection of Outliers in Projection-Based Modeling. Analytical Chemistry (2020, in publish). doi: 10.1021/acs.analchem.9b04611

mdatools documentation built on Aug. 13, 2023, 1:06 a.m.