Streamlining spectral data processing and modeling for spectroscopy applications
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

55 lines
2.4 KiB

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-vip.R
\name{plot_pls_vip}
\alias{plot_pls_vip}
\title{Plot stacked ggplot2 graphs with the Variable Importance for the
Projection (VIP) scores, mean replicate spectra (absorbance) per sample_id,
and the preprocessed spectra.}
\usage{
plot_pls_vip(mout, y1 = "spc_mean", y2 = "spc_pre",
by = "sample_id",
xlab = expression(paste("Wavenumber [", cm^-1, "]")),
ylab1 = "Absorbance", ylab2 = "Preprocessed Abs.",
alpha = 0.2)
}
\arguments{
\item{mout}{Model output list that is returned from
\code{simplerspec::fit_pls()}. This object contains a nested list with
the \code{caret::train()} object (class \code{train}), based on which
VIPs at finally selected number of PLS components are computed.}
\item{y1}{Character vector of list-column name in
\code{mout$data$calibration}, where spectra for bottom graph are extracted.
Default is \code{"spc_mean"}, which plots the mean calibration spectra after
resampling.}
\item{y2}{Character string of list-column name in
\code{mout$data$calibration}, where spectra for bottom graph are extracted.
Default is \code{"spc_pre"}, which plots the preprocessed calibration
spectra after resampling.}
\item{by}{Character string that is used to assign spectra to the same group
and therefore ensures that all spectra are plotted with the same colour.
Default is \code{"sample_id"}}
\item{xlab}{Character string of X axis title for shared x axis of stacked
graphs. Default is \code{expression(paste("Wavenumber [", cm^-1, "]"))}}
\item{ylab1}{Y axis title of bottom spectrum. Default is \code{"Absorbance"}.}
\item{ylab2}{Y axis title of bottom spectrum. Default is
\code{"Preprocessed Abs."}.}
\item{alpha}{Double between 0 and 1 that defines transparency of spectra
lines in returned graph (ggplot plot object).}
}
\description{
Plot stacked ggplot2 graphs of VIP for the final
PLS regression model output of the calibration (training) data set for the
final number of components, raw (replicate mean) spectra, and preprocessed
spectra. Regions with VIP > 1 are highlighted across the stacked graphs
in beige colour rectangles. VIP calculation is implemented as described in
Chong, I.-G., and Jun, C.-H. (2005). Performance of some variable selection
methods when multicollinearity is present. Chemometrics and Intelligent
Laboratory Systems, 78(1--2), 103--112. https://doi.org/10.1016/j.chemolab.2004.12.011
}