Description Usage Arguments Value Note Author(s) See Also Examples

Computes simple approximations to the hypervolume of univariate and multivariate data sets. Also returns the location of the centre of mass.

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`data` |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed, and covariates should not be included. If a matrix or data frame, rows correspond to observations and columns correspond to variables. There |

`method` |
The method used to estimate the hypervolume. The default method uses the function |

`reciprocal` |
A logical variable indicating whether or not the reciprocal hypervolume is desired rather than the hypervolume itself. The default is to return the hypervolume. |

A list with the following two elements:

`vol`

A hypervolume estimate (or its inverse).

This can be used as the hypervolume parameter for the noise component when observations are designated as noise in

`MoE_clust`

.`loc`

A vector of length

`ncol(data)`

giving the location of the centre of mass.This can help in predicting the fitted values of models fitted with noise components via

`MoE_clust`

.

This function is called when adding a noise component to `MoEClust`

models via the function `MoE_control`

, specifically it's argument `noise.meth`

. The function internally only uses the response variables, and not the covariates. However, one can bypass the invocation of this function by specifying its `noise.vol`

argument directly. This is explicitly necessary for models for high-dimensional data which include a noise component for which this function cannot estimate a (hyper)volume.

Note that supplying the volume manually to `MoE_clust`

can affect the summary of the means in `parameters$mean`

and by extension the location of the MVN ellipses in `MoE_gpairs`

plots for models with *both* expert network covariates and a noise component. The location cannot be estimated when the volume is supplied manually; in this case, prediction is made on the basis of renormalising the `z`

matrix after discarding the column corresponding to the noise component. Otherwise, the mean of the noise component is accounted for. The renormalisation approach can be forced by specifying `noise.args$discard.noise=TRUE`

, even when the mean of the noise component is available.

Keefe Murphy - <keefe.murphy@mu.ie>

`hypvol`

, `convhulln`

, `ellipsoidhull`

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