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Plotting lag-sequential models

Mohammed Saqr

Sonsoles López-Pernas

lagdynamics has one plotting verb, plot(fit, type = ), plus dedicated plots for the resampling and comparison results. Every view of a fit displays the same quantity – the adjusted residual, the departure of a transition from chance – and differs only in geometry. The single fit used throughout is the bundled engagement data (138 students, three engagement states).

view	call	backend
residual heatmap	`plot(fit)`	ggplot2
residual network	`plot(fit, type = "network")`	cograph
transition (TNA) network	`plot(fit, type = "network", weights = "tna")`	cograph
chord diagram	`plot(fit, type = "chord")`	cograph
polar sunburst	`plot(fit, type = "sunburst")`	ggplot2
uncertainty forest	`plot(bootstrap_lsa(fit))`, `plot(certainty_lsa(fit))`	ggplot2
group barrel	`plot(compare_lsa(g))`, `plot(bayes_compare_lsa(g))`	ggplot2

Colour. Two conventions are used, by purpose. The residual heatmap, chord, and sunburst use the residual diverging scale (warm = over- represented, cool = avoided). The network and group comparison plots follow the wider Transition Network Analysis (TNA) convention – blue = more than chance, red = less (avoided edges dashed) – so an lsa network reads like any other transition network.

Residual heatmap

The default. Rows are the current state, columns the next; colour is the adjusted residual. which selects the matrix.

plot(fit)                       # adjusted residuals (default)

plot(fit, which = "prob")       # transition probabilities

Residual network

The same residuals as a directed graph: blue edges (solid) are over-represented, red edges (dashed, with a soft halo) are avoided.

plot(fit, type = "network")

Transition network (a TNA model)

Weight the edges by probability and the view becomes the familiar transition network, drawn in the Transition Network Analysis (TNA) style by cograph::splot(): coloured nodes, a per-node initial-probability ring, and weighted directed edges. This shows what happens, where the residual network shows what is surprising.

plot(fit, type = "network", weights = "tna")

Chord and sunburst

A chord diagram of the transition flow, and a polar sunburst of each state’s outgoing distribution.

plot(fit, type = "chord")

plot(fit, type = "sunburst")                 # rose (default)

plot(fit, type = "sunburst", style = "wedge")  # frequency wedges

Uncertainty forests

A fitted edge is one estimate; the forest shows its interval. Both the resampling bootstrap and the analytic certainty plot as a circular forest of per-edge intervals.

plot(bootstrap_lsa(fit, R = 200))            # resampling CIs

plot(certainty_lsa(fit))                     # analytic credible intervals

Group comparison

For a real grouping we use the bundled long event log group_regulation_long, and fit one model per achievement group.

gfit <- lsa(group_regulation_long, actor = "Actor",
            action = "Action", time = "Time", group = "Achiever")

plot() on a comparison draws a back-to-back barrel: each group’s bar runs to one side, the higher group’s bar is bordered (darker for a larger difference), and the centre chip carries the difference p-value.

cmp <- compare_lsa(gfit, R = 500)
plot(cmp)                                    # frequency-ranked rows

plot(cmp, rank = "effect")                   # surface avoided (red) edges

plot(cmp, style = "heatmap")                 # full-grid difference

The Bayesian comparison reuses the same barrel, with the credible posterior in place of the permutation p-value.

plot(bayes_compare_lsa(gfit, seed = 1))

Grouped fits

Every type works on a grouped fit, drawing one panel per group (combined = FALSE, the default) or a single tiled figure (combined = TRUE).

plot(gfit)                                   # one heatmap per group

The worker functions

plot(fit, type = ) is a front door over exported workers, each with its own full argument list:

plot_transitions(fit, weights = "residuals", significant = TRUE)  # network
plot_chords(fit, width = "prob")                                  # chord
plot_polar(fit, style = "wedge")                                  # sunburst
plot_forest(bootstrap_lsa(fit), n_top = 12)                       # forest

In short

plot(fit)                                   # residual heatmap (default)
plot(fit, type = "network")                 # residual network (blue = more)
plot(fit, type = "network", weights = "tna")   # transition network (a TNA model)
plot(fit, type = "chord"); plot(fit, type = "sunburst")
plot(bootstrap_lsa(fit)); plot(certainty_lsa(fit))   # uncertainty forests
plot(compare_lsa(gfit)); plot(bayes_compare_lsa(gfit))  # group barrels
plot(gfit)                                  # grouped: one panel per group

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.