nir: Near-infrared data with temperature effects

In e-sensing/kohonenDTW: Supervised and Unsupervised Self-Organising Maps for Satellite Image Time Series

Description

A data object containing near-infrared spectra of ternary mixtures of ethanol, water and iso-propanol, measured at five different temperatures (30, 40, ..., 70 degrees Centigrade).

References

F. Wulfert , W.Th. Kok, A.K. Smilde: Anal. Chem. 1998, 1761-1767

Examples

  data(nir)

  set.seed(3)
  nirnet <- xyf(X = nir$spectra[nir$training,],
                Y = nir$composition[nir$training,],
                user.weights = c(3,1),
                grid = somgrid(6, 6, "hexagonal"), rlen=500)
  plot(nirnet, "counts", main="Counts")

  ## Focus on compound 2 (water):
  par(mfrow = c(1,2))
  set.seed(13)
  nirnet <- xyf(X = nir$spectra[nir$training,],
                Y = nir$composition[nir$training, 2, drop = FALSE],
                grid = somgrid(6, 6, "hexagonal"), rlen=500)
  water.xyf <-
    predict(nirnet, newdata = nir$spectra[nir$training,],
            unit.predictions = getCodes(nirnet, 2),
            whatmap = 1)$prediction
  plot(nirnet, "property", property = water.xyf[[1]],
       main="Prediction of water content")
  ## Plot temperatures as circles
  symbols(nirnet$grid$pts[nirnet$unit.classif,] +
          matrix(rnorm(sum(nir$training)*2, sd=.1), ncol=2),
	  circles = (nir$temperature[nir$training] - 20)/250,
	  inches = FALSE, add = TRUE)

  ## Model temperatures
  set.seed(13)
  nirnet2 <- xyf(X = nir$spectra[nir$training,],
                 Y = matrix(nir$temperature[nir$training], ncol = 1),
                 user.weights = c(1,3),
                 grid = somgrid(6, 6, "hexagonal"), rlen=500)
  temp.xyf <- predict(nirnet2, newdata = nir$spectra[nir$training,],
                      unit.predictions = getCodes(nirnet2, 2),
                      whatmap = 1)$prediction

  plot(nirnet2, "property", property = temp.xyf[[1]],
       palette.name = rainbow,
       main="Prediction of temperatures")
  ## Plot concentrations of water as circles
  symbols(nirnet2$grid$pts[nirnet2$unit.classif,] +
          matrix(rnorm(sum(nir$training)*2, sd=.1), ncol=2),
	  circles = 0.05 + 0.4 * nir$composition[nir$training,2],
	  inches = FALSE, add = TRUE)

e-sensing/kohonenDTW documentation built on May 27, 2019, 3:29 p.m.