Description Usage Arguments Details Value Author(s) Examples

Computes correlation and moving correlation dissimilarity matrices.

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`Xr` |
a matrix. |

`Xu` |
an optional matrix containing data of a second set of observations. |

`ws` |
for moving correlation dissimilarity, an odd integer value which
specifies the window size. If |

`center` |
a logical indicating if the spectral data |

`scale` |
a logical indicating if |

The correlation dissimilarity \mjeqndd between two observations \mjeqnx_ix_i and \mjeqnx_jx_j is based on the Perason's correlation coefficient (\mjeqn\rho\rho) and it can be computed as follows:

\mjdeqnd(x_i, x_j) = \frac12((1 - \rho(x_i, x_j)))d(x_i, x_j) = 1/2 (1 - \rho(x_i, x_j))

The above formula is used when `ws = NULL`

.
On the other hand (when `ws != NULL`

) the moving correlation
dissimilarity between two observations \mjeqnx_ix_i and \mjeqnx_jx_j
is computed as follows:

d(x_i, x_j; ws) = \frac12 ws\sum_k=1^p-ws1 - \rho(x_i,(k:k+ws), x_j,(k:k+ws))d(x_i, x_j) = 1/(2 ws)\sum_(k=1)^p-ws(1 - \rho(x_(i,k:k+ws), x_(j,k:k+ws)))

where \mjeqnwsws represents a given window size which rolls sequentially from 1 up to \mjeqnp - wsp - ws and \mjeqnpp is the number of variables of the observations.

The function does not accept input data containing missing values.

a matrix of the computed dissimilarities.

Antoine Stevens and Leonardo Ramirez-Lopez

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