• seriograph() produces a Ford diagram highlighting the relationships between rows and columns.

• eppm() computes for each cell of a numeric matrix the positive difference from the column mean percentage.

Usage

seriograph(object, ...)

eppm(object, ...)

# S4 method for matrix
eppm(object)

# S4 method for data.frame
eppm(object)

# S4 method for matrix
seriograph(object, weights = FALSE)

# S4 method for data.frame
seriograph(object, weights = FALSE)

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.

weights

A logical scalar: should row weights (i.e. the number of observations divided by the total number of observations) be displayed?

Value

• seriograph() returns a ggplot2::ggplot object.

• eppm() returns a numeric matrix.

Details

The 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).

seriograph() superimposes the frequencies (grey) and EPPM values (black) for each row-column pair in a Ford diagram.

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 .

plot_ford()

Other plot methods: matrigraph(), plot_bertin(), plot_diceleraas(), plot_diversity, plot_ford(), plot_heatmap(), plot_rank(), plot_spot()

N. Frerebeau

Examples

data("compiegne", package = "folio")

## Seriograph
seriograph(compiegne)

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