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 \(m \times p\)
numericmatrixordata.frameof count data (absolute frequencies giving the number of individuals for each class).- ...
Currently not used.
- weights
A
logicalscalar: 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.
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 .
See also
Other plot methods:
matrigraph(),
plot_bertin(),
plot_diceleraas(),
plot_diversity,
plot_ford(),
plot_heatmap(),
plot_rank(),
plot_spot()
Examples
## Data from Desachy 2004
data("compiegne", package = "folio")
## Seriograph
seriograph(compiegne)
## Compute EPPM
counts_eppm <- eppm(compiegne)
plot_heatmap(counts_eppm) +
khroma::scale_fill_YlOrBr(name = "EPPM")
