• matrigraph() produces a heatmap highlighting the deviations from independence.

• pvi() computes for each cell of a numeric matrix the percentage to the column theoretical independence value.

## Usage

matrigraph(object, ...)

pvi(object, ...)

# S4 method for matrix
pvi(object)

# S4 method for data.frame
pvi(object)

# S4 method for matrix
matrigraph(object, reverse = FALSE, axes = TRUE, ...)

# S4 method for data.frame
matrigraph(object, reverse = FALSE, ...)

## 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.

reverse

A logical scalar: should negative deviations be centered (see details)?

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

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

• pvi() returns a numeric matrix.

## Details

PVI (in french "pourcentages de valeur d'indépendance") is calculated for each cell as the percentage to the column theoretical independence value: PVI greater than $$1$$ represent positive deviations from the independence, whereas PVI smaller than $$1$$ represent negative deviations (Desachy 2004).

The PVI matrix allows to explore deviations from independence (an intuitive approach to $$\chi^2$$), in such a way that a high-contrast matrix has quite significant deviations, with a low risk of being due to randomness (Desachy 2004).

matrigraph() displays the deviations from independence:

• If the PVI is equal to $$1$$ (statistical independence), the cell of the matrix is filled in grey.

• If the PVI is less than $$1$$ (negative deviation from independence), the size of the grey square is proportional to the PVI (the white margin thus represents the fraction of negative deviation).

• If the PVI is greater than $$1$$ (positive deviation), a black square representing the fraction of positive deviations is superimposed. For large positive deviations (PVI greater than $$2$$), the cell in filled in black.

If reverse is TRUE, the fraction of negative deviations is displayed as a white square.

## 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 .

plot_heatmap()

Other plot methods: plot_bertin(), plot_diceleraas(), plot_ford(), plot_heatmap(), plot_rank(), plot_spot(), seriograph()

N. Frerebeau

## Examples

## Data from Desachy 2004
data("compiegne", package = "folio")

## Matrigraph
matrigraph(compiegne)

matrigraph(compiegne, reverse = TRUE)

## Compute PVI
counts_pvi <- pvi(compiegne)
plot_heatmap(counts_pvi, col = khroma::color("iridescent")(12))