pRoloc (version 1.12.4)

nnetOptimisation: nnet parameter optimisation

Description

Classification parameter optimisation for artificial neural network algorithm.

Usage

nnetOptimisation(object, fcol = "markers", decay = c(0, 10^(-1:-5)), size = seq(1, 10, 2), 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.

decay

The hyper-parameter. Default values are c(0, 10^(-1:-5)).

size

The hyper-parameter. Default values are seq(1, 10, 2).

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 xval macro F1 matrices.

seed

The optional random number generator seed.

verbose

A logical defining whether a progress bar is displayed.

...

Additional parameters passed to nnet from package nnet.

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

nnetClassification and example therein.