pcvr (version 1.2.0)

pcv.plsr: Run Partial Least Squares Regression on spectral data

Description

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, ...)

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.

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.

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)))
}