perTurboOptimisation: PerTurbo parameter optimisation
In pRoloc: A unifying bioinformatics framework for spatial proteomics

Description Usage Arguments Details Value Author(s) See Also

View source: R/machinelearning-functions-PerTurbo.R

Classification parameter optimisation for the PerTurbo algorithm

perTurboOptimisation(object, fcol = "markers", pRegul = 10^(seq(from =
  -1, to = 0, by = 0.2)), sigma = 10^(seq(from = -1, to = 1, by = 0.5)),
  inv = c("Inversion Cholesky", "Moore Penrose", "solve", "svd"),
  reg = c("tikhonov", "none", "trunc"), times = 1, test.size = 0.2,
  xval = 5, fun = mean, seed, verbose = TRUE)

`object`	An instance of class `"MSnSet"`.
`fcol`	The feature meta-data containing marker definitions. Default is `markers`.
`pRegul`	The hyper-parameter for the regularisation (values are in ]0,1] ). If reg =="trunc", pRegul is for the percentage of eigen values in matrix. If reg =="tikhonov", then 'pRegul' is the parameter for the tikhonov regularisation. Available configurations are : "Inversion Cholesky" - ("tikhonov" / "none"), "Moore Penrose" - ("tikhonov" / "none"), "solve" - ("tikhonov" / "none"), "svd" - ("tikhonov" / "none" / "trunc").
`sigma`	The hyper-parameter.
`inv`	The type of algorithm used to invert the matrix. Values are : "Inversion Cholesky" (`chol2inv`), "Moore Penrose" (`ginv`), "solve" (`solve`), "svd" (`svd`). Default value is `"Inversion Cholesky"`.
`reg`	The type of regularisation of matrix. Values are "none", "trunc" or "tikhonov". Default value is `"tikhonov"`.
`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 `times` macro F1 matrices.
`seed`	The optional random number generator seed.
`verbose`	A `logical` defining whether a progress bar is displayed.

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.

An instance of class "GenRegRes".

Thomas Burger and Samuel Wieczorek

perTurboClassification and example therein.

pRoloc documentation built on Nov. 8, 2020, 6:26 p.m.