Classification parameter optimisation for the partial least square distcriminant analysis algorithm.
Usage
plsdaOptimisation(
object,
fcol = "markers",
ncomp = 2:6,
times = 100,
test.size = 0.2,
xval = 5,
fun = mean,
seed,
verbose = TRUE,
...
)Arguments
- object
An instance of class
"MSnSet".- fcol
The feature meta-data containing marker definitions. Default is
markers.- ncomp
The hyper-parameter. Default values are
2:6.- times
The number of times internal cross-validation is performed. Default is 100.
- test.size
The size of test data. Default is 0.2 (20 percent).
- xval
The
n-cross validation. Default is 5.- fun
The function used to summarise the
xvalmacro F1 matrices.- seed
The optional random number generator seed.
- verbose
A
logicaldefining whether a progress bar is displayed.- ...
Additional parameters passed to
plsdafrom packagecaret.
Value
An instance of class "GenRegRes".
Details
Note that when performance scores precision, recall and (macro) F1 are calculated, any NA values are replaced by 0. This decision is motivated by the fact that any class that would have either a NA precision or recall would result in an NA F1 score and, eventually, a NA macro F1 (i.e. mean(F1)). Replacing NAs by 0s leads to F1 values of 0 and a reduced yet defined final macro F1 score.
See also
plsdaClassification and example therein.