perTurboClassification {pRoloc} | R Documentation

## perTurbo classification

### Description

Classification using the PerTurbo algorithm.

### Usage

    perTurboClassification(object, assessRes, scores = c("prediction", "all",
      "none"), pRegul, sigma, inv, reg, fcol = "markers")

### Arguments

`object` | An instance of class `"MSnSet"`.
`assessRes` | An instance of class `"GenRegRes"`, as generated by `svmRegularisation`.
`scores` | One of `"prediction"`, `"all"` or `"none"` to report the score for the predicted class only, for all classes or none.
`pRegul` | If `assessRes` is missing, a `pRegul` must be provided. See `perTurboOptimisation` for details.
`sigma` | If `assessRes` is missing, a `sigma` must be provided. See `perTurboOptimisation` for details.
`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"`.
`fcol` | The feature meta-data containing marker definitions. Default is `markers`.

### Value

An instance of class `"MSnSet"` with `perTurbo` and `perTurbo.scores` feature variables storing the classification results and scores respectively.

### Author(s)

Thomas Burger and Samuel Wieczorek

### References

N. Courty, T. Burger, J. Laurent. "PerTurbo: a new classification algorithm based on the spectrum perturbations of the Laplace-Beltrami operator", The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), D. Gunopulos et al. (Eds.): ECML PKDD 2011, Part I, LNAI 6911, pp. 359 - 374, Athens, Greece, September 2011.

### Examples

    library(pRolocdata)
    data(dunkley2006)
    ## reducing parameter search space
    params <- perTurboOptimisation(dunkley2006,
                                   pRegul = 2^seq(-2,2,2),
                                   sigma = 10^seq(-1, 1, 1),
                                   inv = "Inversion Cholesky",
                                   reg ="tikhonov",
                                   times = 3)
    params
    plot(params)
    f1Count(params)
    levelPlot(params)
    getParams(params)
    res <- perTurboClassification(dunkley2006, params)
    getPredictions(res, fcol = "perTurbo")
    getPredictions(res, fcol = "perTurbo", t = 0.75)
    plot2D(res, fcol = "perTurbo")

[Package pRoloc version 1.30.0 Index]