tabula (version 1.6.1)

independance: Independance

Description

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 numeric matrix.

EPPM

This positive difference from the column mean percentage (in french "<U+00E9>cart 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).

Details

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

References

Desachy, B. (2004). Le s<U+00E9>riographe EPPM: un outil informatis<U+00E9> de s<U+00E9>riation graphique pour tableaux de comptages. Revue arch<U+00E9>ologique de Picardie, 3(1), 39-56. 10.3406/pica.2004.2396.

See Also

plot_ford(), plot_heatmap(), seriate_rank()

Other statistics: test_diversity()

Examples

# NOT RUN {
## 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_heatmap(counts_eppm)
# }