pls.GEM: Partial Least Squares modelling of GEM objects.

In gemR: General Effect Modelling

Partial Least Squares modelling of GEM objects.

Description

The output of GEM is used as input to a PLS classification with the selected effect as response. It is possible to compare two models using the gem2 argument. Variable selection is available through Jackknifing (from package pls) and Shaving (from package plsVarSel).

Usage

## S3 method for class 'GEM'
pls(
  object,
  effect,
  ncomp,
  newdata = NULL,
  gem2,
  validation,
  jackknife = NULL,
  shave = NULL,
  df.used = object$df.used,
  ...
)

Arguments

object	Object of class GEM.
effect	The effect to be used as response.
ncomp	Number of PLS components.
newdata	Optional new data matrix for prediction.
gem2	Second object of class GEM for comparison.
validation	Optional validation parameters for plsr.
jackknife	Optional argument specifying if jackknifing should be applied.
shave	Optional argument indicating if variable shaving should be used. shave should be a list with two elements: the PLS filter method and the proportion to remove. shave = TRUE uses defaults: list("sMC", 0.2).
df.used	Optional argument indicating how many degrees of freedom have been consumed during deflation. Default value from input object.
...	Additional arguments for plsr.

Details

If using the shave options, the segment type is given as type instead of segment.type (see examples).

Value

An object of class GEMpls, mvr, list containing the fitted PLS model, classifications/predictions, data and optionally Jackknife or Shaving results.

See Also

Analyses using GEM: elastic, pca, sca, neuralnet. Confidence interval plots: confints. Convenience knock-in and knock-out of effects: knock.in.

Examples

data(MS, package = "gemR")
# Subset to reduce runtime in example
MS$proteins <- MS$proteins[,20:70]

gem <- GEM(proteins ~ MS * group, data = MS[-1,])

# Simple PLS using interleaved cross-validation
plsMod <- pls(gem, 'MS', 6, validation = "CV",
              segment.type = "interleaved", length.seg = 25)
plot(plsMod)
scoreplot(plsMod, labels = "names")

# PLS with shaving of variables (mind different variable for cross-validation type)
plsModS <- pls(gem, 'MS', 6, validation = "CV",
              type = "interleaved", length.seg=25, shave = TRUE)
# Error as a function of remaining variables
plot(plsModS)
# Selected variables for minimum error
with(plsModS$shave, colnames(X)[variables[[min.red+1]]])

 # Time consuming due to leave-one-out cross-validation
  plsModJ <- pls(gem, 'MS', 5, validation = "LOO",
              jackknife = TRUE)
  colSums(plsModJ$classes == as.numeric(MS$MS[-1]))
  # Jackknifed coefficient P-values (sorted)
  plot(sort(plsModJ$jack[,1,1]), pch = '.', ylab = 'P-value')
  abline(h=c(0.01,0.05),col=2:3)

  scoreplot(plsModJ)
  scoreplot(plsModJ, comps=c(1,3))   # Selected components
  # Use MS categories for colouring and clusters for plot characters.
  scoreplot(plsModJ, col = gem$symbolicDesign[['MS']],
                  pch = 20+as.numeric(gem$symbolicDesign[['group']]))
  loadingplot(plsModJ, scatter=TRUE) # scatter=TRUE for scatter plot

gemR documentation built on Sept. 9, 2025, 5:57 p.m.