pls {ER}

R Documentation

Partial Least Squares modelling of ER objects.

Description

Partial Least Squares modelling of ER objects.

Usage

pls(er, ...)

## S3 method for class '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.

See Also

ER, elastic and confints.

Examples

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

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


plsMod <- pls(er, 'MS', 5, validation = "LOO",
              type = "interleaved", length.seg=25, jackknife = TRUE)
colSums(plsMod$classes == as.numeric(MS$MS[-1]))
# Jackknifed coefficient P-values (sorted)
plot(sort(plsMod$jack[,1,1]), pch = '.', ylab = 'P-value')
abline(h=c(0.01,0.05),col=2:3)

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

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