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  • 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,
  fill = "darkgrey",
  border = NA,
  axes = TRUE,
  ...
)

# S4 method for data.frame
seriograph(
  object,
  weights = FALSE,
  fill = "darkgrey",
  border = NA,
  axes = TRUE,
  ...
)

Arguments

object

A \(m \times p\) numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table).

...

Currently not used.

weights

A logical scalar: should the row sums be displayed?

fill

The color for filling the bars.

border

The color to draw the borders.

axes

A logical scalar: should axes be drawn on the plot? It will omit labels where they would abut or overlap previously drawn labels.

Value

  • seriograph() is called for its side-effects: it results in a graphic being displayed (invisibly returns 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 .

See also

Author

N. Frerebeau

Examples

## 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))