This vignette demonstrates the basic usage of the
cograph package using a transition probability matrix from
a learning analytics study.
We use a simulated Markov transition matrix representing student learning behavior transitions across nine states.
states <- c("Read", "Watch", "Try", "Ask", "Discuss",
"Review", "Search", "Reflect", "Submit")
set.seed(42)
mat <- matrix(c(
0.00, 0.25, 0.15, 0.00, 0.10, 0.00, 0.08, 0.00, 0.00,
0.10, 0.00, 0.30, 0.00, 0.00, 0.12, 0.00, 0.00, 0.00,
0.00, 0.10, 0.00, 0.20, 0.00, 0.00, 0.00, 0.15, 0.25,
0.05, 0.00, 0.10, 0.00, 0.30, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.15, 0.00, 0.20, 0.00, 0.18, 0.00,
0.12, 0.08, 0.00, 0.00, 0.00, 0.00, 0.10, 0.00, 0.20,
0.00, 0.00, 0.15, 0.00, 0.00, 0.10, 0.00, 0.00, 0.12,
0.00, 0.00, 0.10, 0.00, 0.12, 0.00, 0.00, 0.00, 0.28,
0.00, 0.00, 0.00, 0.00, 0.00, 0.10, 0.00, 0.05, 0.00
), nrow = 9, byrow = TRUE)
rownames(mat) <- colnames(mat) <- statessplot(mat, layout = "circle", node_size = 9,
node_shape = c("circle", "square", "triangle", "diamond",
"star", "pentagon", "hexagon", "heart", "circle"),
node_fill = palette_colorblind(9))