perTurboOptimisation: PerTurbo parameter optimisation

In lgatto/pRoloc: A unifying bioinformatics framework for spatial proteomics

PerTurbo parameter optimisation

Description

Classification parameter optimisation for the PerTurbo algorithm

Usage

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
)

Arguments

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.

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.

Value

An instance of class "GenRegRes".

Author(s)

Thomas Burger and Samuel Wieczorek

See Also

perTurboClassification and example therein.

lgatto/pRoloc documentation built on Oct. 23, 2024, 12:51 a.m.