Plots a Bertin, Ford (battleship curve) or Dice-Leraas diagram.

## Usage

```
plot_bertin(object, ...)
plot_ford(object, ...)
# S4 method for matrix
plot_bertin(object, threshold = NULL, scale = NULL)
# S4 method for matrix
plot_ford(object)
# S4 method for CountMatrix
plot_ford(object, EPPM = FALSE)
```

## Arguments

- object
An abundance matrix to be plotted.

- ...
Currently not used.

- threshold
A

`function`

that takes a numeric vector as argument and returns a numeric threshold value (see below). If`NULL`

(the default), no threshold is computed.- scale
A

`function`

used to scale each variable, that takes a numeric vector as argument and returns a numeric vector. If`NULL`

(the default), no scaling is performed.- EPPM
A

`logical`

scalar: should the EPPM be drawn (see below)?

## Value

A ggplot2::ggplot object.

## Details

If `EPPM`

is `TRUE`

and if a relative abundance is greater than
the mean percentage of the type, the exceeding part is highlighted.

## Bertin Matrix

As de Falguerolles *et al.* (1997) points out:
"In abstract terms, a Bertin matrix is a matrix
of displays. ... To fix ideas, think of a data matrix, variable by case,
with real valued variables. For each variable, draw a bar chart of variable
value by case. High-light all bars representing a value above some sample
threshold for that variable."

## EPPM

This 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 graphical tool to explore significance
of relationship between rows and columns related to seriation (Desachy
2004).

## References

Bertin, J. (1977). *La graphique et le traitement graphique de
l'information*. Paris: Flammarion. Nouvelle Bibliothèque Scientifique.

de Falguerolles, A., Friedrich, F. & Sawitzki, G. (1997). A Tribute to J.
Bertin's Graphical Data Analysis. In W. Badilla & F. Faulbaum (eds.),
*SoftStat '97: Advances in Statistical Software 6*. Stuttgart: Lucius
& Lucius, p. 11-20.

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
.

Ford, J. A. (1962). *A quantitative method for deriving cultural
chronology*. Washington, DC: Pan American Union. Technical manual 1.

## See also

Other plot:
`plot_diversity`

,
`plot_line`

,
`plot_matrix`

,
`plot_spot()`

## Examples

```
# \donttest{
## Abundance data
## Coerce dataset to a count matrix
data("mississippi", package = "folio")
counts1 <- as_count(mississippi)
## Plot a Bertin diagram...
## ...without threshold
plot_bertin(counts1)
## ...with variables scaled to 0-1 and the variable mean as threshold
scale_01 <- function(x) (x - min(x)) / (max(x) - min(x))
plot_bertin(counts1, threshold = mean, scale = scale_01)
## Abundance data
## Coerce dataset to a count matrix (data from Desachy 2004)
data("compiegne", package = "folio")
counts2 <- as_count(compiegne)
## Plot a Ford diagram...
## ...without threshold
plot_ford(counts2)
## ...with EPPM
plot_ford(counts2, EPPM = TRUE)
# }
```