independance | R Documentation |

Independance

eppm(object, ...) pvi(object, ...) ## S4 method for signature 'CountMatrix' eppm(object) ## S4 method for signature 'CountMatrix' pvi(object)

`object` |
A CountMatrix object. |

`...` |
Currently not used. |

Computes for each cell of a numeric matrix one of the following statistic.

A `numeric`

`matrix`

.

This positive difference from the column mean percentage (in french "écart
positif au pourcentage moyen", EPPM) represents a deviation from the
situation of statistical independence. As independence can be interpreted as
the absence of relationships between types and the chronological order of
the assemblages, `EPPM`

is a useful graphical tool to explore significance
of relationship between rows and columns related to seriation (Desachy
2004).

`PVI`

is calculated for each cell as the percentage to the column
theoretical independence value: `PVI`

greater than *1* represent
positive deviations from the independence, whereas `PVI`

smaller than
*1* represent negative deviations (Desachy 2004).

The `PVI`

matrix allows to explore deviations from independence
(an intuitive graphical approach to *Chi-squared*),
in such a way that a high-contrast matrix has quite significant deviations,
with a low risk of being due to randomness (Desachy 2004).

N. Frerebeau

Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation
graphique pour tableaux de comptages. *Revue archéologique de Picardie*,
3(1), 39-56. doi: 10.3406/pica.2004.2396.

`plot_ford()`

, `plot_heatmap()`

, `seriate_rank()`

Other statistics:
`test_diversity()`

## Abundance data ## Coerce dataset to a count matrix (data from Desachy 2004) data("compiegne", package = "folio") counts <- as_count(compiegne) ## Compute EPPM counts_eppm <- eppm(counts) ## Compute PVI counts_pvi <- pvi(counts) plot(counts_eppm)

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