`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.

## 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_ford()`

,
`plot_heatmap()`

,
`plot_rank()`

,
`plot_spot()`

## 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))
```