knntlOptimisation: theta parameter optimisation
In pRoloc: A unifying bioinformatics framework for spatial proteomics

Description Usage Arguments Details Value Author(s) References See Also

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

Classification parameter optimisation for the KNN implementation of Wu and Dietterich's transfer learning schema

knntlOptimisation(primary, auxiliary, fcol = "markers", k, times = 50,
  test.size = 0.2, xval = 5, by = 0.5, length.out, th, xfolds,
  BPPARAM = BiocParallel::bpparam(), method = "Breckels",
  log = FALSE, seed)

`primary`	An instance of class `"MSnSet"`.
`auxiliary`	An instance of class `"MSnSet"`.
`fcol`	The feature meta-data containing marker definitions. Default is `markers`.
`k`	Numeric vector of length 2, containing the best `k` parameters to use for the primary (`k[1]`) and auxiliary (`k[2]`) datasets. See `knnOptimisation` for generating best `k`.
`times`	The number of times cross-validation is performed. Default is 50.
`test.size`	The size of test (validation) data. Default is 0.2 (20 percent).
`xval`	The number of rounds of cross-validation to perform.
`by`	The increment for theta, must be one of `c(1, 0.5, 0.25, 0.2, 0.15, 0.1, 0.05)`
`length.out`	Alternative to using `by` parameter. Specifies the desired length of the sequence of theta to test.
`th`	A matrix of theta values to test for each class as generated from the function `thetas`, the number of columns should be equal to the number of classes contained in `fcol`. Note: columns will be ordered according to `getMarkerClasses(primary, fcol)`. This argument is only valid if the default method 'Breckels' is used.
`xfolds`	Option to pass specific folds for the cross validation.
`BPPARAM`	Required for parallelisation. If not specified selects a default `BiocParallelParam`, from global options or, if that fails, the most recently registered() back-end.
`method`	The k-NN transfer learning method to use. The default is 'Breckels' as described in the Breckels et al (2016). If 'Wu' is specificed then the original method implemented Wu and Dietterich (2004) is implemented.
`log`	A `logical` defining whether logging should be enabled. Default is `FALSE`. Note that logging produes considerably bigger objects.
`seed`	The optional random number generator seed.

knntlOptimisation implements a variation of Wu and Dietterich's transfer learning schema: P. Wu and T. G. Dietterich. Improving SVM accuracy by training on auxiliary data sources. In Proceedings of the Twenty-First International Conference on Machine Learning, pages 871 - 878. Morgan Kaufmann, 2004. A grid search for the best theta is performed.

A list of containing the theta combinations tested, associated macro F1 score and accuracy for each combination over each round (specified by times).

Lisa Breckels

Breckels LM, Holden S, Wonjar D, Mulvey CM, Christoforou A, Groen AJ, Kohlbacher O, Lilley KS, Gatto L. Learning from heterogeneous data sources: an application in spatial proteomics. bioRxiv. doi: http://dx.doi.org/10.1101/022152

Wu P, Dietterich TG. Improving SVM Accuracy by Training on Auxiliary Data Sources. Proceedings of the 21st International Conference on Machine Learning (ICML); 2004.

knntlClassification and example therein.

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