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