ER (version 1.1.1)

pls: Partial Least Squares modelling of ER objects.

Description

The output of ER is used as input to a PLS classification with the selected effect as response. It is possible to compare two models using the er2 argument. Variable selection is available through Jackknifing (from package pls) and Shaving (from package plsVarSel).

Usage

pls(er, ...)
# S3 method for ER
pls(
  er,
  effect,
  ncomp,
  newdata = NULL,
  er2,
  validation,
  jackknife = NULL,
  shave = NULL,
  df.used = NULL,
  ...
)

Arguments

er

Object of class ER.

...

Additional arguments for plsr.

effect

The effect to be used as response.

ncomp

Number of PLS components.

newdata

Optional new data matrix for prediction.

er2

Second object of class ER 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.

Details

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

See Also

ER, elastic and confints.

Examples

data(MS, package = "ER")
er <- ER(proteins ~ MS * cluster, data = MS[-1,])

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

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

 # Time consuming due to leave-one-out cross-validation
plsModJ <- pls(er, '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 = er$symbolicDesign[['MS']],
                  pch = 20+as.numeric(er$symbolicDesign[['cluster']]))
loadingplot(plsModJ, scatter=TRUE) # scatter=TRUE for scatter plot