eppm(object, ...) pvi(object, ...) # S4 method for CountMatrix eppm(object) # S4 method for CountMatrix pvi(object)
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
EPPM is a useful graphical tool to explore significance
of relationship between rows and columns related to seriation (Desachy
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).
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 .
## 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)