Description Usage Arguments Details Value Examples

View source: R/hyperoverlap_detect.r

Given a matrix containing the ecological data (x) and labels (y) for two entities, a support vector machine is trained and the predicted label of each point is evaluated. If every point has been classified correctly, the entities can be separated and they do not overlap.

1 2 | ```
hyperoverlap_detect(x, y, kernel = "polynomial", kernel.degree = 3, cost = 500,
stoppage.threshold = 0.4, verbose = TRUE, set = FALSE)
``` |

`x` |
A matrix or data.frame containing the variables of interest for both entities. |

`y` |
A vector of labels. |

`kernel` |
Character. Either "linear" or "polynomial" (default = "polynomial"). |

`kernel.degree` |
Parameter needed for |

`cost` |
Specifies the SVM margin 'hardness'. Default value is 50, but can be increased for improved accuracy (although this increases runtimes and memory usage). |

`stoppage.threshold` |
Numeric. If the number of points misclassified using a linear hyperplane exceeds this proportion of the number of observations, non-linear separation is not attempted. Must be between 0 and 1 (default = 0.2). |

`verbose` |
Logical. If TRUE, prints diagnostic messages. |

`set` |
Logical. Is this function being called as part of |

Input data should be preprocessed so that all variables are comparable (e.g. same order of magnitude). Polynomial kernels allow curvilinear decision boundaries to be found between entities (see https://www.cs.cmu.edu/~ggordon/SVMs/new-svms-and-kernels.pdf). Smaller values of `kernel.degree`

permit less complex decision boundaries; biological significance is likely to be lost at values > 5.

A `hyperoverlap-class`

object

1 2 |

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