Description Usage Arguments Details Value Examples

View source: R/hyperoverlap_set.r

This function is a wrapper for `hyperoverlap_detect`

for pairwise overlap detection between multiple entities.

1 2 3 4 | ```
hyperoverlap_set(x, y, kernel = "polynomial",kernel.degree = 3, cost = 1000,
stoppage.threshold = 0.2, write.to.file = FALSE,
path = NULL,
sample.dimensionality.omit = "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 1000, 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). |

`write.to.file` |
Logical. If TRUE, each |

`path` |
Character. Path to write .rds files to. Ignored if |

`sample.dimensionality.omit` |
Logical. If TRUE, omits any entity pairs with a combined sample size less than n+1, where n is the number of dimensions (see details). |

In n dimensions, any set of points up to n+1 points can be separated using a linear hyperplane. This may produce an artefactual non-overlap result.
The `sample.dimensionality.omit`

parameter gives two options for dealing with these pairs when they form part of a larger analysis.
If `sample.dimensionality.omit = "TRUE"`

, this pair is removed from the analysis (result = NA).
If `sample.dimensionality.omit = "FALSE"`

, the pair is included, but a warning is printed.

A long-form matrix with the following columns:
entity1,
entity2,
shape,
polynomial.order (if `kernel="polynomial"`

),
result,
number.of.points.misclassified.

If specified, individual `Hyperoverlap-class`

objects are written to file.

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