| seriograph | R Documentation | 
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.
seriograph(object, ...)
eppm(object, ...)
## S4 method for signature 'matrix'
eppm(object)
## S4 method for signature 'data.frame'
eppm(object)
## S4 method for signature 'matrix'
seriograph(
  object,
  weights = FALSE,
  fill = "darkgrey",
  border = NA,
  axes = TRUE,
  ...
)
## S4 method for signature 'data.frame'
seriograph(
  object,
  weights = FALSE,
  fill = "darkgrey",
  border = NA,
  axes = TRUE,
  ...
)
| object | A  | 
| ... | Currently not used. | 
| weights | A  | 
| fill | The color for filling the bars. | 
| border | The color to draw the borders. | 
| axes | A  | 
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.
seriograph() is called for its side-effects: it results in a graphic
being displayed (invisibly returns object).
eppm() returns a numeric matrix.
N. Frerebeau
Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3406/pica.2004.2396")}.
plot_ford()
Other plot methods: 
matrigraph(),
plot_bertin(),
plot_diceleraas(),
plot_ford(),
plot_heatmap(),
plot_rank(),
plot_spot()
## Data from Desachy 2004
data("compiegne", package = "folio")
## Seriograph
seriograph(compiegne)
seriograph(compiegne, weights = TRUE)
## Compute EPPM
counts_eppm <- eppm(compiegne)
plot_heatmap(counts_eppm, col = khroma::color("YlOrBr")(12))
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