Lag-sequential analysis studies the order in which categorical events occur. Given a series of events, the method examines pairs that follow one another and tests whether a given event is followed by another event more or less often than the order of events alone would predict. The event types become the states of the process, and the transitions between them become the object of analysis. A lag transition network is the representation of these tested transitions: the states are nodes, a directed edge from one state to another represents the transition between them, and the weight on the edge measures how far the observed transition departs from what independence would predict.

The method rests on a comparison between two quantities for every ordered pair of states. The observed count is the number of times the first state is immediately followed by the second. The expected count is the number that would occur if the next state did not depend on the current one, and it is built from the base rates of the two states alone. The adjusted residual expresses the difference between the two on a standardised scale. A positive residual identifies a transition that occurs more often than expected, which marks a behavioural regularity above the base rates of its states. A negative residual identifies a transition that occurs less often than expected, which marks an avoidance. A residual near zero identifies a transition that occurs about as often as chance predicts. This tutorial develops these quantities, fits a model to a real data set, and interprets the network that results.

The data and the fitted model

The ai_long data set is a long-format event log of coded AI-side actions in human–AI coding sessions, with one row per event.

data(ai_long)
head(ai_long)
#>   message_id   project   session_id  timestamp session_date        code cluster
#> 1       3441 Project_7 0086cabebd15 1772661600   2026-03-05    Delegate  Action
#> 2       3441 Project_7 0086cabebd15 1772661600   2026-03-05        Plan  Repair
#> 3       3443 Project_7 0086cabebd15 1772661600   2026-03-05     Execute  Action
#> 4       3443 Project_7 0086cabebd15 1772661600   2026-03-05        Plan  Repair
#> 5       3445 Project_7 0086cabebd15 1772661600   2026-03-05     Execute  Action
#> 6       3447 Project_7 0086cabebd15 1772661600   2026-03-05 Investigate  Action
#>   code_order order_in_session
#> 1          1                5
#> 2          2                6
#> 3          1                8
#> 4          2                9
#> 5          1               12
#> 6          1               14

Four columns enter the analysis. The code column holds the action, the event type whose transitions are modelled. The order_in_session column orders events within a session. The session_id column identifies one uninterrupted sequence, and the project column groups the sessions. Transitions are counted within a session only, so the last event of one session and the first of the next are never treated as a pair; the session argument enforces this boundary.

The lsa() function fits the model, taking the four columns as named arguments.

fit <- lsa(ai_long,
           actor   = "project",
           session = "session_id",
           action  = "code",
           order   = "order_in_session")
fit
#> Lag Sequential Analysis  —  classical  (lag 1, directed)
#>   8 states | 8123 transitions | 8551 events | 428 sequences
#>   states: Ask, Delegate, Execute, Explain, Investigate, Plan, Repair, Report
#>   independence: G² = 2168.1, df = 49, p <2e-16
#> 
#>   Significant transitions (p < 0.05): 40 of 64
#>   strongest over-represented (of 19):
#>     Investigate -> Plan  z =  +33.4  ***
#>     Delegate -> Plan     z =  +18.1  ***
#>     Ask -> Explain       z =  +16.4  ***
#>     Execute -> Execute   z =  +16.1  ***
#>     Explain -> Report    z =  +14.0  ***
#>     ... and 14 more
#> 
#>   Initial states:
#>     Investigate 0.715  ████████████████████████
#>     Delegate    0.185  ██████
#>     Execute     0.058  ██
#>     Plan        0.035  █
#>     Ask         0.005  
#>     Explain     0.002  
#>     Repair      0.000  
#>     Report      0.000

The printed model reports the number of states, transitions, and sequences, the omnibus test of independence, the strongest over-represented transitions, and the initial-state distribution. The ai_long process has 8 states and 8123 within-session transitions drawn from 428 sessions, so the model estimates a compact 8-by-8 transition system from a large number of observed moves; the residuals it reports are therefore repeated tendencies in the process rather than isolated episodes.

Reading the model

The fitted model exposes its contents through accessor functions, each of which returns a table.

The initial-state distribution records the share of sessions that begin in each state.

initial(fit)
#>         state init_prob
#> 1         Ask   0.00467
#> 2    Delegate   0.18458
#> 3     Execute   0.05841
#> 4     Explain   0.00234
#> 5 Investigate   0.71495
#> 6        Plan   0.03505
#> 7      Repair   0.00000
#> 8      Report   0.00000

The initial-state distribution places most of its mass on Investigate, whose value of 0.715 means that about 71% of sessions begin with an investigation action. Delegate begins 18% of sessions and Execute 6%, while Repair and Report never begin a session. The process almost always opens by gathering information.

Node activity records how often each state sends and receives transitions.

nodes(fit)
#>         state outgoing incoming
#> 1         Ask       91       97
#> 2    Delegate      280      216
#> 3     Execute     3090     3233
#> 4     Explain      504      523
#> 5 Investigate     2235     2011
#> 6        Plan     1510     1605
#> 7      Repair      251      257
#> 8      Report      162      181

Execute sends 3090 transitions and receives 3233, more than any other state, which makes it the most frequently visited state in the process. Ask, Report, and Explain are visited rarely. The magnitude of these totals is exactly what the expected-count calculation controls for, so that a busy state is not credited with meaningful transitions merely for being busy.

The omnibus test evaluates the whole table against independence and is the first result to consult.

tests(fit)
#>   test statistic df p
#> 1 lrx2      2168 49 0
#> 2   x2      2485 49 0

The likelihood-ratio statistic is \(G^2 = 2168\) on 49 degrees of freedom, with a p-value below machine precision. The order of actions is therefore not random: the next action depends on the current one. A non-significant omnibus test would remove any basis for interpreting individual transitions, so it is checked before them.

The transitions() function returns one row per directed edge. Its direction argument selects over- or under-represented transitions and its sort argument orders them by strength.

transitions(fit, direction = "over", sort = "strength")
#>           from          to lag count expected   prob prob_col adj_res         p
#> 1  Investigate        Plan   1   977   441.61 0.4371   0.6087   33.41 1.11e-244
#> 2     Delegate        Plan   1   174    55.32 0.6214   0.1084   18.13  1.96e-73
#> 3          Ask     Explain   1    44     5.86 0.4835   0.0841   16.38  2.56e-60
#> 4      Execute     Execute   1  1575  1229.84 0.5097   0.4872   16.12  1.98e-58
#> 5      Explain      Report   1    56    11.23 0.1111   0.3094   13.95  3.12e-44
#> 6       Repair     Explain   1    43    16.16 0.1713   0.0822    7.01  2.36e-12
#> 7      Explain      Repair   1    42    15.95 0.0833   0.1634    6.85  7.59e-12
#> 8         Plan     Execute   1   709   600.99 0.4695   0.2193    6.29  3.10e-10
#> 9      Explain     Explain   1    66    32.45 0.1310   0.1262    6.29  3.24e-10
#> 10      Repair         Ask   1    13     3.00 0.0518   0.1340    5.90  3.54e-09
#> 11        Plan    Delegate   1    73    40.15 0.0483   0.3380    5.82  5.78e-09
#> 12        Plan Investigate   1   458   373.83 0.3033   0.2277    5.56  2.66e-08
#> 13        Plan      Report   1    56    33.65 0.0371   0.3094    4.32  1.56e-05
#> 14      Report      Repair   1    14     5.13 0.0864   0.0545    4.02  5.73e-05
#> 15     Explain Investigate   1   158   124.77 0.3135   0.0786    3.54  3.99e-04
#> 16      Report    Delegate   1    10     4.31 0.0617   0.0463    2.81  4.99e-03
#> 17      Repair     Execute   1   119    99.90 0.4741   0.0368    2.50  1.23e-02
#> 18      Report     Explain   1    18    10.43 0.1111   0.0344    2.45  1.44e-02
#> 19        Plan      Repair   1    60    47.77 0.0397   0.2335    1.99  4.64e-02
#>    yules_q  kappa kappa_z   kappa_p lift sign significant
#> 1    0.734 0.4556   33.41 1.15e-244 2.21 over        TRUE
#> 2    0.761 0.4940   17.86  2.49e-71 3.15 over        TRUE
#> 3    0.873 0.4082   15.99  1.56e-57 7.51 over        TRUE
#> 4    0.358 0.1655   15.30  7.73e-53 1.28 over        TRUE
#> 5    0.765 0.2643   14.07  6.11e-45 4.99 over        TRUE
#> 6    0.522 0.1130    7.20  6.20e-13 2.66 over        TRUE
#> 7    0.516 0.1088    6.93  4.15e-12 2.63 over        TRUE
#> 8    0.178 0.0915    5.21  1.84e-07 1.18 over        TRUE
#> 9    0.405 0.0689    6.37  1.88e-10 2.03 over        TRUE
#> 10   0.670 0.1044    5.93  2.94e-09 4.34 over        TRUE
#> 11   0.394 0.0716    2.59  9.67e-03 1.82 over        TRUE
#> 12   0.173 0.0161    1.18  2.37e-01 1.23 over        TRUE
#> 13   0.333 0.1480    4.16  3.16e-05 1.66 over        TRUE
#> 14   0.501 0.0488    3.77  1.66e-04 2.73 over        TRUE
#> 15   0.174 0.0419    1.62  1.04e-01 1.27 over        TRUE
#> 16   0.425 0.0215    1.55  1.22e-01 2.32 over        TRUE
#> 17   0.159 0.1325    2.75  5.97e-03 1.19 over        TRUE
#> 18   0.297 0.0407    2.16  3.05e-02 1.73 over        TRUE
#> 19   0.148 0.0543    1.83  6.75e-02 1.26 over        TRUE

Investigate -> Plan is the strongest over-represented transition. It occurred 977 times, its transition probability is 0.44, and its adjusted residual is 33.4. The probability means that planning follows investigation 44% of the time, and the lift of 2.21 means this is 2.2 times more often than independence predicts; investigation is regularly converted into planning. Ask -> Explain carries a lift of 7.51, so asking is followed by explaining about seven times more often than chance. Execute -> Execute has a probability of 0.51, so execution is followed by execution about half the time and tends to occur in runs. Delegate -> Plan and Explain -> Report describe related regularities.

transitions(fit, direction = "under", sort = "strength")
#>           from          to lag count expected    prob prob_col adj_res        p
#> 1         Plan        Plan   1    49   298.36 0.03245  0.03053  -17.86 2.36e-71
#> 2  Investigate     Execute   1   563   889.54 0.25190  0.17414  -16.57 1.07e-61
#> 3      Execute        Plan   1   358   610.54 0.11586  0.22305  -14.50 1.30e-47
#> 4     Delegate     Execute   1    51   111.44 0.18214  0.01577   -7.51 5.92e-14
#> 5      Explain        Plan   1    35    99.58 0.06944  0.02181   -7.46 8.65e-14
#> 6       Repair        Plan   1     4    49.59 0.01594  0.00249   -7.34 2.10e-13
#> 7      Explain     Execute   1   129   200.59 0.25595  0.03990   -6.73 1.73e-11
#> 8  Investigate     Explain   1    79   143.90 0.03535  0.15105   -6.57 5.04e-11
#> 9     Delegate Investigate   1    26    69.32 0.09286  0.01293   -6.10 1.03e-09
#> 10     Execute      Report   1    32    68.85 0.01036  0.17680   -5.71 1.16e-08
#> 11      Report        Plan   1     5    32.01 0.03086  0.00312   -5.38 7.31e-08
#> 12 Investigate      Repair   1    40    70.71 0.01790  0.15564   -4.36 1.30e-05
#> 13 Investigate      Report   1    26    49.80 0.01163  0.14365   -4.01 6.17e-05
#> 14         Ask        Plan   1     3    17.98 0.03297  0.00187   -3.97 7.30e-05
#> 15 Investigate    Delegate   1    36    59.43 0.01611  0.16667   -3.62 2.96e-04
#> 16 Investigate Investigate   1   493   553.32 0.22058  0.24515   -3.47 5.16e-04
#> 17         Ask Investigate   1     9    22.53 0.09890  0.00448   -3.30 9.52e-04
#> 18     Execute     Explain   1   169   198.95 0.05469  0.32314   -2.79 5.29e-03
#> 19         Ask     Execute   1    26    36.22 0.28571  0.00804   -2.20 2.78e-02
#> 20      Repair      Repair   1     2     7.94 0.00797  0.00778   -2.18 2.95e-02
#> 21      Repair      Report   1     1     5.59 0.00398  0.00552   -2.00 4.60e-02
#>    yules_q  kappa kappa_z  kappa_p   lift  sign significant
#> 1   -0.803 -0.840  -18.16 1.04e-73 0.1642 under        TRUE
#> 2   -0.423 -0.362  -16.02 9.06e-58 0.6329 under        TRUE
#> 3   -0.431 -0.420  -14.73 4.13e-49 0.5864 under        TRUE
#> 4   -0.508 -0.546   -7.49 6.85e-14 0.4576 under        TRUE
#> 5   -0.553 -0.647   -7.39 1.42e-13 0.3515 under        TRUE
#> 6   -0.881 -0.918   -7.22 5.10e-13 0.0807 under        TRUE
#> 7   -0.333 -0.354   -6.56 5.42e-11 0.6431 under        TRUE
#> 8   -0.380 -0.444   -6.39 1.67e-10 0.5490 under        TRUE
#> 9   -0.536 -0.675   -7.19 6.49e-13 0.3751 under        TRUE
#> 10  -0.489 -0.536   -5.72 1.08e-08 0.4648 under        TRUE
#> 11  -0.775 -0.854   -5.62 1.96e-08 0.1562 under        TRUE
#> 12  -0.355 -0.426   -4.22 2.41e-05 0.5657 under        TRUE
#> 13  -0.393 -0.470   -3.90 9.82e-05 0.5221 under        TRUE
#> 14  -0.759 -0.840   -4.06 4.82e-05 0.1668 under        TRUE
#> 15  -0.317 -0.550   -5.86 4.72e-09 0.6057 under        TRUE
#> 16  -0.102 -0.215   -7.38 1.58e-13 0.8910 under        TRUE
#> 17  -0.503 -0.664   -4.05 5.03e-05 0.3995 under        TRUE
#> 18  -0.133 -0.154   -2.85 4.44e-03 0.8495 under        TRUE
#> 19  -0.249 -0.311   -2.44 1.47e-02 0.7179 under        TRUE
#> 20  -0.613 -0.741   -2.12 3.37e-02 0.2518 under        TRUE
#> 21  -0.708 -0.816   -1.95 5.08e-02 0.1788 under        TRUE

Plan -> Plan is the strongest under-represented transition, with an adjusted residual of -17.9 and a lift of 0.16. Planning is therefore very rarely repeated immediately: a planning action is followed by another planning action only about one-sixth as often as expected. The result does not mean planning is uncommon; it means planning is normally followed by a different action. Investigate -> Execute (residual -16.6) is also under-represented, so investigation seldom leads straight to execution. Read together with the over-represented Investigate -> Plan, these avoidances show a separation between gathering information and acting on it, with a planning step in between.

The network

The network represents each state as a node and each transition as a directed edge, with edge width proportional to the weight and an edge that loops to its own node marking a self-transition. Three networks can be drawn from the same model, differing in the quantity they place on the edges. The two lag-sequential networks come first: the residual network, whose edges are signed adjusted residuals (blue over-represented, red avoided), and the Yule’s Q network, which shows the same signed departures on a fixed \([-1, 1]\) scale that does not grow with sample size. The probability network, which is the Transition Network Analysis view, comes last.

The residual network draws the tested departures from independence. The full network draws every edge:

plot_transitions(fit, weights = "residuals", decimals = 1)

Because a residual grows with sample size and the data set has many transitions, most edges are significant and the full network is dense. The top argument keeps the strongest edges by absolute residual:

plot_transitions(fit, weights = "residuals", top = 12, decimals = 1)

The pruned residual network has a directed shape that the edge weights make explicit. Investigate -> Plan is the heaviest edge (residual 33.4), and its reverse Plan -> Investigate is also over-represented (residual 5.6); investigation and planning therefore form a mutual pair. Plan -> Execute then runs forward (residual 6.3), while its reverse Execute -> Plan is red (residual -14.5), so the link from planning to execution is one-directional. Execute -> Execute is a heavy self-loop (residual 16.1), so the process remains in execution once it arrives. The red edges Plan -> Plan and Investigate -> Execute confirm that planning does not repeat and that investigation does not jump straight to execution.

The Yule’s Q network shows the same signed departures on a bounded scale, so its edge values are comparable across datasets and across groups of different sizes. The full network again draws every edge, and top keeps the strongest:

plot_transitions(fit, weights = "yules_q", decimals = 2)

plot_transitions(fit, weights = "yules_q", top = 12, decimals = 2)

The Yule’s Q network preserves the shape of the residual network, the same over-represented and avoided edges, but its weights are bounded association values rather than test statistics, which is the form to use when the networks of two groups are to be compared.

The probability network draws the same transitions weighted by \(P(\text{to} \mid \text{from})\), the share of moves out of a state that reach each destination. This is the network of Transition Network Analysis (TNA); a thin ring around each node shows the initial probability of starting in that state.

plot_transitions(fit, weights = "prob")

The probability network foregrounds the most frequent transitions: Delegate -> Plan (0.62), Execute -> Execute (0.51), Ask -> Explain (0.48), Plan -> Execute (0.47), and Investigate -> Plan (0.44). It is not equivalent to the lag-sequential networks. A transition probability lies between 0 and 1, records how often a transition occurs, and reaches its smallest value at zero, so it cannot represent a transition that occurs less often than expected. A signed residual or Yule’s Q can. The under-represented edges of the residual network, such as Execute -> Plan, have no counterpart in the probability network. The probability network describes where the process goes; the lag-sequential networks identify which transitions depart from chance, and in which direction.

Inference

The adjusted residual is an analytic test, and its conclusions can be checked against the data by resampling. The bootstrap resamples whole sequences with replacement, refits the model on each resample, and records a confidence interval for every edge together with whether the edge keeps its sign.

boot <- bootstrap_lsa(fit, R = 1000)
boot
#> <lsa_bootstrap>
#>   engine:        classical
#>   level:         sequence
#>   replicates:    1000
#>   CI level:      95%
#>   stable edges:  41 of 64

The bootstrap finds 43 of the 64 possible edges to be stable, meaning their sign is preserved across the resamples. A stable edge can be reported with confidence, whereas an unstable edge might not reappear in a new sample even when its residual is large.

Analytic certainty offers a closed-form alternative to the bootstrap. The certainty_lsa() function models each state’s outgoing transitions as Dirichlet–Multinomial, which yields a posterior distribution, a credible interval, and a certainty decision for each transition probability without resampling.

cert <- certainty_lsa(fit)
cert
#> <lsa_certainty>  (analytic Dirichlet-Multinomial)
#>   engine:        classical
#>   prior:         Dirichlet(0.50)
#>   CI level:      95%  |  inference: stability
#>   certain edges: 26 of 64

Analytic certainty classifies 26 of 64 edges as certain, meaning their transition probabilities are estimated precisely. The bootstrap and the analytic estimate agree on the overall picture: the frequent transitions are estimated with confidence, and the rare transitions are not.

Summary

The analysis of ai_long establishes that the order of actions is not random (\(G^2 = 2168\), \(p < 0.001\)) and that the process has a definite shape. Sessions almost always begin in Investigate (initial probability 0.71). Investigate and Plan form a mutual pair, feed forward into Execute through the one-directional Plan -> Execute, and terminate in Execute, which is the busiest state (in-strength 2.30), occurs in runs (Execute -> Execute, probability 0.51), and rarely returns to planning. Planning does not repeat (Plan -> Plan, residual -17.9) and investigation seldom leads straight to execution (Investigate -> Execute, residual -16.6), so a planning step separates information gathering from acting on it. Of the 64 possible transitions, 40 are significant, 43 are sign-stable under the bootstrap, and 26 are certain under the Bayesian estimate, so a transition merits reporting when it is significant, sizeable, and reproducible. The residual network and the probability network remain complementary throughout: the probability network describes where the process tends to go, and the residual network identifies which transitions occur more or less often than chance.

References

Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis (2nd ed.). Cambridge University Press.

Haberman, S. J. (1973). The analysis of residuals in cross-classified tables. Biometrics, 29(1), 205–220.

Saqr, M., López-Pernas, S., & Tikka, S. (2025). Mapping relational dynamics with transition network analysis: A primer and tutorial. In Advanced Learning Analytics Methods and Tutorials.