lagseq 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 = "prob")`	tna
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 TNA / Nestimate convention – blue = more than chance, red = less (avoided edges dashed) – so an lsa network reads like any other transition network in that ecosystem.

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: lagseq builds a tna object on the fly and renders it with tna’s styling. This shows what happens, where the residual network shows what is surprising.

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

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 a long event log, tna::group_regulation_long, and fit one model per achievement group.

gfit <- lsa(tna::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 = "prob")  # 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

Plotting lag-sequential models