Independance

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

object | A |
---|---|

... | Currently not used. |

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

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

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()`

N. Frerebeau

## 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)