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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