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tabula (version 1.8.0)

independance: Independance

Description

Independance

Usage

eppm(object, ...)

pvi(object, ...)

# S4 method for matrix eppm(object)

# S4 method for data.frame eppm(object)

# S4 method for matrix pvi(object)

# S4 method for data.frame pvi(object)

Value

A numeric

matrix.

Arguments

object

A \(m \times p\) numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each class).

...

Currently not used.

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 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 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).

Author

N. Frerebeau

Details

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

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. tools:::Rd_expr_doi("10.3406/pica.2004.2396").

See Also

plot_ford(), plot_heatmap(), seriate_rank()

Other statistics: test_diversity()

Examples

Run this code
data("cantabria")

## Compute EPPM
counts_eppm <- eppm(cantabria)
plot_heatmap(counts_eppm) +
  khroma::scale_fill_YlOrBr(name = "EPPM")

## Compute PVI
counts_pvi <- pvi(cantabria)
plot_heatmap(counts_pvi) +
  khroma::scale_fill_BuRd(name = "PVI", midpoint = 1)

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