The plotMatrix
function generates a visual representation of a pattern causality matrix using different methods. It allows users to visualize the positive, negative, or dark causality effects in a specified matrix, providing insight into the relationships between items.
Arguments
- pcmatrix
A list containing three matrices (
positive
,negative
, anddark
) which represent the respective causality types for different items.- status
A character string specifying which causality matrix to plot. Must be one of
"positive"
,"negative"
, or"dark"
.- method
A character string specifying the visualization method for the plot. Options include
"circle"
,"square"
,"ellipse"
,"number"
,"shade"
,"color"
, and"pie"
.
Value
A visual plot of the selected causality matrix using the specified method. The plot provides a color-coded representation of the causality strengths between items.
Examples
# \donttest{
data(climate_indices)
dataset <- climate_indices[,-1]
pcmatrix <- pcMatrix(dataset, E = 3, tau = 1, metric = "euclidean", h = 1, weighted = TRUE)
#> CAUSE: AO
#> EFFECT: AO
#> EFFECT: AAO
#> EFFECT: NAO
#> EFFECT: PNA
#> CAUSE: AAO
#> EFFECT: AO
#> EFFECT: AAO
#> EFFECT: NAO
#> EFFECT: PNA
#> CAUSE: NAO
#> EFFECT: AO
#> EFFECT: AAO
#> EFFECT: NAO
#> EFFECT: PNA
#> CAUSE: PNA
#> EFFECT: AO
#> EFFECT: AAO
#> EFFECT: NAO
#> EFFECT: PNA
#> Calculation duration: 7.88062763214111
plotMatrix(pcmatrix, status = "positive", method = "color")
# }