pls | R Documentation |
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
).
pls(er, ...) ## S3 method for class 'ER' pls( er, effect, ncomp, newdata = NULL, er2, validation, jackknife = NULL, shave = NULL, df.used = NULL, ... )
er |
Object of class |
... |
Additional arguments for |
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 |
validation |
Optional validation parameters for |
jackknife |
Optional argument specifying if jackknifing should be applied. |
shave |
Optional argument indicating if variable shaving should be used. |
df.used |
Optional argument indicating how many degrees of freedom have been consumed during deflation. Default value from input object. |
If using the shave
options, the segment type is given as type
instead of segment.type
(see examples).
ER
, elastic
and confints
.
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
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