Independance

Independance

Usage

eppm(object, ...)

pvi(object, ...)

# S4 method for CountMatrix
eppm(object)

# S4 method for CountMatrix
pvi(object)

Arguments

object

A CountMatrix object.

...

Currently not used.

Value

A numericmatrix.

Details

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

EPPM

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

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 \(\chi^2\)), in such a way that a high-contrast matrix has quite significant deviations, with a low risk of being due to randomness (Desachy 2004).

References

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 .

See also

plot_ford(), plot_heatmap(), seriate_rank()

Other statistics: test_diversity()

Author

N. Frerebeau

Examples

## 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)