seriograph {tabula} | R Documentation |
Seriograph
Description
-
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 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,
...
)
Arguments
object |
A |
... |
Currently not used. |
weights |
A |
fill |
The color for filling the bars. |
border |
The color to draw the borders. |
axes |
A |
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.
Value
-
seriograph()
is called for its side-effects: it results in a graphic being displayed (invisibly returnsobject
).
Author(s)
N. Frerebeau
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_rarefaction
,
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))