Plot Q and T-squared Contributions

Plots the contributions of the variables to the Q residual or to the \(T^2\) of samples (see contributions()) against wavelength, one panel per sample, and labels the wavelengths that contribute most, with the emission lines they match when a line list is given.

Usage

plot_contributions(
  x,
  samples = NULL,
  top = 10,
  lines = NULL,
  tol = 0.1,
  spectra = NULL,
  span = 5,
  interactive = FALSE,
  title = NULL
)

Arguments

x: An object returned by contributions().
samples: The samples to show (row numbers of x, or names in its sample column). Default is the three with the largest statistic.
top: The number of peaks labeled per component (per panel). Default is 10; use 0 for no labels.
lines: Optional line list returned by libs_lines(), to label the peaks with the emission lines they match.
tol: The largest distance, in nm, between a peak and a line it matches. Default is 0.1.
spectra: Optional spectra (a data frame or matrix with the variables of the model, other columns being ignored) or a single spectrum (a named numeric vector), whose mean is drawn in grey behind each panel, rescaled to it, to show whether a peak is on an emission line.
span: The half-width, in channels, of the window in which a peak is a local extremum. Default is 5.
interactive: If TRUE, the plot is made with plotly, with the peaks and their candidate lines shown on hover. Default is FALSE.
title: The plot title.

Value

A ggplot object, or a plotly object if interactive = TRUE.

Details

Each contribution is drawn red above zero (the sample is higher than the model, or than the reference samples) and blue below. The top largest positive and negative local extrema are labeled, as in plot_loadings(): in bold with the nearest line of lines within tol nm. The mean spectrum can be drawn in grey behind each panel, rescaled to it.

Author

Christian L. Goueguel

Examples

spectra_id <- forageLIBS |> dplyr::select(1:2) |> names()
minerals <- forageLIBS |> dplyr::select(3:14) |> names()
spectra <- forageLIBS |> dplyr::select(-dplyr::all_of(c(spectra_id, minerals)))
set.seed(1)
fit <- robpca(spectra, k = 3)
q <- contributions(fit, data = spectra, samples = c(49, 127), reference = "regular")
plot_contributions(q, spectra = spectra)