RDocumentation: mcrals

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if(!require('mdatools')) {
    install.packages('mdatools')
    library('mdatools')
}

library(mdatools)

# resolve mixture of carbonhydrates Raman spectra

data(carbs)

# define constraints for contributions
cc <- list(
   constraint("nonneg")
)

# define constraints for spectra
cs <- list(
   constraint("nonneg"),
   constraint("norm", params = list(type = "area"))
)

# because by default initial approximation is made by using random numbers
# we need to seed the generator in order to get reproducable results
set.seed(6)

# run ALS
m <- mcrals(carbs$D, ncomp = 3, cont.constraints = cc, spec.constraints = cs)
summary(m)

# plot cumulative and individual explained variance
par(mfrow = c(1, 2))
plotVariance(m)
plotCumVariance(m)

# plot resolved spectra (all of them or individually)
par(mfrow = c(2, 1))
plotSpectra(m)
plotSpectra(m, comp = 2:3)

# plot resolved contributions (all of them or individually)
par(mfrow = c(2, 1))
plotContributions(m)
plotContributions(m, comp = 2:3)

# of course you can do this manually as well, e.g. show original
# and resolved spectra
par(mfrow = c(1, 1))
mdaplotg(
   list(
      "original" = prep.norm(carbs$D, "area"),
      "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area")
   ), col = c("gray", "red"), type = "l"
)

# in case if you have reference spectra of components you can compare them with
# the resolved ones:
par(mfrow = c(3, 1))
for (i in 1:3) {
   mdaplotg(
      list(
         "pure" = prep.norm(mda.subset(mda.t(carbs$S), 1), "area"),
         "resolved" = prep.norm(mda.subset(mda.t(m$resspec), 1), "area")
      ), col = c("gray", "red"), type = "l", lwd = c(3, 1)
   )
}

# See bookdown tutorial for more details.