Run Partial Least Squares Regression on spectral data

Partial Least Squares Regression (plsr) is often used to analyze spectral data.

Usage

pcv.plsr(df, resps = NULL, spectra = NULL, train = 0.8, cv = 10, ...)

Arguments

df: Data frame containing metadata and spectral histogram data
resps: Vector of response variables.
spectra: Either one column name (in the case of long data) or a set of columns in the case of wide data. If a single character string is provided and it is not one of the column names then it is taken to be a pattern that will match some set of column names in the data to use (see examples).
train: Proportion of data to use as training data.
cv: Number of cross validation iterations.
...: Further arguments passed to caret::train.

Value

a list of lists each with model performance, prediction target, model, plot, N components, and variable influence on projection components for each response variable.

Details

Note that columns that sum to 0 in the training or test data will be removed. This function also uses the 'pls' method from the pls package.

Examples


if (rlang::is_installed("pls")) {
  dists <- list(
    rlnorm = list(meanlog = log(40), sdlog = 0.5),
    rlnorm = list(meanlog = log(60), sdlog = 0.35)
  )
  mv <- mvSim(
    dists = dists, n_samples = 100, counts = 1000,
    min_bin = 1, max_bin = 180, wide = TRUE
  )
  sv <- growthSim("logistic",
    n = 5, t = 20,
    params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
  )
  d <- cbind(sv, mv[, -1])
  # note that this requires the "pls" package to be installed.
  x <- pcv.plsr(df = d, resps = "y", spectra = grepl("^sim_", colnames(d)))
}