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Interoperability: wide data, long logs,
tna, and Nestimate
Mohammed Saqr
Sonsoles López-Pernas
lagdynamics is designed to sit inside the same
event-sequence workflow as Transition Network Analysis (TNA),
Nestimate, cograph, and other sequence tools.
The important interoperability promise is simple:
- the same
lsa() front door accepts wide sequence data,
raw long event logs, tna sequence objects, and
Nestimate prepared objects;
- long logs use the same column grammar as TNA-style workflows:
actor, action, time,
order, and session;
- fitted models expose transition probabilities, initial
probabilities, tidy transition tables, and a
cograph_network object for rendering.
Same grammar as TNA
In a TNA workflow, a raw event log is usually sequenced by naming the
actor, the event/action code, and either a time or order column.
lsa() uses the same grammar directly.
prepare_data(log, actor = "Actor", action = "Action", time = "Time")
lsa(log, actor = "Actor", action = "Action", time = "Time")
The column names are passed as strings. action is the
only mandatory long-format column. If neither actor nor
session is supplied, the log is treated as one long
sequence. If session is supplied, each session is a
sequence. If both actor and session are
supplied, each actor-session pair is a sequence.
Wide sequence data
Wide data is the simplest input: rows are sequences and columns are
ordered time points. This is the form used by
engagement.
head(engagement)
#> 1 2 3 4 5 6 7
#> 1 Active Disengaged Disengaged Disengaged Active Active Active
#> 2 Average Average Average Average Average Average Average
#> 3 Average Active Active Active Active Active Active
#> 4 Active Active Active Active Active Average Average
#> 5 Active Active Active Average Active Average Active
#> 6 Average Average Disengaged Average Disengaged Disengaged Average
#> 8 9 10 11 12 13 14 15
#> 1 Active Average Active Average Active <NA> <NA> <NA>
#> 2 Average Average Active Average Average Average Average Average
#> 3 Active Active Average Active Active Active Active Active
#> 4 Active Active Active Average Active Average Active Average
#> 5 Active Active Disengaged Active Active Active Average Average
#> 6 Average Average Disengaged Average Disengaged Average Average Average
fit_wide <- lsa(engagement)
fit_wide
#> Lag Sequential Analysis - classical (lag 1, directed)
#> 3 states | 1734 transitions | 1870 events | 136 sequences
#> states: Active, Average, Disengaged
#> independence: G² = 618.3, df = 4, p <2e-16
#>
#> Significant transitions (p < 0.05): 7 of 9
#> strongest over-represented (of 3):
#> Active -> Active z = +21.7 ***
#> Disengaged -> Disengaged z = +15.4 ***
#> Average -> Average z = +12.5 ***
#>
#> Initial states:
#> Active 0.382 ████████████████████████
#> Average 0.368 ███████████████████████
#> Disengaged 0.250 ████████████████
The result is read through the same verbs used everywhere else.
transitions(fit_wide, significant = TRUE)
#> from to lag count expected prob prob_col adj_res p
#> 1 Active Active 1 459 247 0.698 0.7051 21.7 4.60e-104
#> 2 Average Active 1 153 282 0.204 0.2350 -12.9 4.16e-38
#> 3 Disengaged Active 1 39 122 0.120 0.0599 -10.5 5.11e-26
#> 4 Active Average 1 176 290 0.267 0.2307 -11.3 1.06e-29
#> 5 Average Average 1 458 330 0.610 0.6003 12.5 1.35e-35
#> 6 Active Disengaged 1 23 121 0.035 0.0719 -12.6 3.64e-36
#> 7 Disengaged Disengaged 1 157 60 0.483 0.4906 15.4 1.90e-53
#> yules_q kappa kappa_z kappa_p lift sign significant
#> 1 0.828 0.444 19.19 4.68e-82 1.858 over TRUE
#> 2 -0.601 -0.499 -14.70 6.81e-49 0.543 under TRUE
#> 3 -0.698 -0.707 -11.46 2.03e-30 0.320 under TRUE
#> 4 -0.534 -0.424 -12.48 9.39e-36 0.608 under TRUE
#> 5 0.553 0.227 9.83 7.97e-23 1.386 over TRUE
#> 6 -0.826 -0.827 -13.41 5.14e-41 0.189 under TRUE
#> 7 0.754 0.314 13.56 7.34e-42 2.618 over TRUE
nodes(fit_wide)
#> state outgoing incoming
#> 1 Active 658 651
#> 2 Average 751 763
#> 3 Disengaged 325 320
initial(fit_wide)
#> state init_prob
#> 1 Active 0.382
#> 2 Average 0.368
#> 3 Disengaged 0.250
Long event logs
Long logs have one row per event. The bundled
group_regulation_long data shows the TNA-style grammar
directly: actor, action, time, and a grouping column.
head(group_regulation_long)
#> Actor Achiever Group Course Time Action
#> 1 1 High 1 A 2025-01-01 10:27:07 cohesion
#> 2 1 High 1 A 2025-01-01 10:35:20 consensus
#> 3 1 High 1 A 2025-01-01 10:42:18 discuss
#> 4 1 High 1 A 2025-01-01 10:50:00 synthesis
#> 5 1 High 1 A 2025-01-01 10:52:25 adapt
#> 6 1 High 1 A 2025-01-01 10:57:31 consensus
Fit a single model from the raw log by naming the columns.
fit_long <- lsa(group_regulation_long, actor = "Actor",
action = "Action", time = "Time")
fit_long
#> Lag Sequential Analysis - classical (lag 1, directed)
#> 9 states | 25533 transitions | 27533 events | 2000 sequences
#> states: adapt, cohesion, consensus, coregulate, discuss, emotion, monitor, plan, synthesis
#> independence: G² = 13203.8, df = 64, p <2e-16
#>
#> Significant transitions (p < 0.05): 72 of 81
#> strongest over-represented (of 23):
#> emotion -> cohesion z = +58.2 ***
#> discuss -> synthesis z = +48.0 ***
#> synthesis -> adapt z = +38.8 ***
#> consensus -> coregulate z = +35.3 ***
#> consensus -> plan z = +32.6 ***
#> ... and 18 more
#>
#> Initial states:
#> consensus 0.214 ████████████████████████
#> plan 0.204 ███████████████████████
#> discuss 0.175 ████████████████████
#> emotion 0.151 █████████████████
#> monitor 0.144 ████████████████
#> cohesion 0.060 ███████
#> synthesis 0.019 ██
#> coregulate 0.019 ██
#> adapt 0.011 █
If the log already has session identifiers, use session.
In the Nestimate-derived ai_long data,
project and session_id define the sequence
boundary and order_in_session defines event order.
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
fit_session <- lsa(ai_long, actor = "project", session = "session_id",
action = "code", order = "order_in_session")
fit_session
#> 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
Grouping uses the same call. The grouping column must be fixed within
each recovered sequence.
gfit <- lsa(group_regulation_long, actor = "Actor", action = "Action",
time = "Time", group = "Achiever")
gfit
#> <lsa_group>
#> engine: classical
#> states: 9 (adapt, cohesion, consensus, coregulate, discuss, emotion, monitor, plan, synthesis)
#> groups: 2
#> - High: 1000 sequences
#> - Low: 1000 sequences
transitions(gfit, significant = TRUE) |> head(6)
#> group from to lag count expected prob prob_col adj_res p
#> 1 High consensus adapt 1 14 38.13 0.00413 0.0979 -4.59 4.45e-06
#> 2 High coregulate adapt 1 20 10.05 0.02237 0.1399 3.27 1.06e-03
#> 3 High discuss adapt 1 48 22.52 0.02396 0.3357 5.88 4.00e-09
#> 4 High emotion adapt 1 5 17.42 0.00323 0.0350 -3.19 1.40e-03
#> 5 High plan adapt 1 4 32.51 0.00138 0.0280 -5.72 1.06e-08
#> 6 High synthesis adapt 1 40 3.13 0.14388 0.2797 21.21 7.61e-100
#> yules_q kappa kappa_z kappa_p lift sign significant
#> 1 -0.544 -0.6606 -4.98 6.36e-07 0.367 under TRUE
#> 2 0.371 0.0636 2.90 3.68e-03 1.990 over TRUE
#> 3 0.466 0.1803 5.21 1.86e-07 2.132 over TRUE
#> 4 -0.589 -0.7375 -3.46 5.48e-04 0.287 under TRUE
#> 5 -0.824 -0.8859 -5.99 2.04e-09 0.123 under TRUE
#> 6 0.905 0.2406 19.62 1.08e-85 12.800 over TRUE
tna objects
lsa() accepts a real tna model when that
model carries its source sequences. The example below starts from the
same included wide sequence data, fits a TNA model with
tna, and then hands that fitted object to
lsa().
tna_fit <- tna::tna(engagement)
tna_fit
#> State Labels :
#>
#> Active, Average, Disengaged
#>
#> Transition Probability Matrix :
#>
#> Active Average Disengaged
#> Active 0.698 0.267 0.035
#> Average 0.204 0.610 0.186
#> Disengaged 0.120 0.397 0.483
#>
#> Initial Probabilities :
#>
#> Active Average Disengaged
#> 0.382 0.368 0.250
fit_from_tna <- lsa(tna_fit)
fit_from_tna
#> Lag Sequential Analysis - classical (lag 1, directed)
#> 3 states | 1734 transitions | 1870 events | 136 sequences
#> states: Active, Average, Disengaged
#> independence: G² = 618.3, df = 4, p <2e-16
#>
#> Significant transitions (p < 0.05): 7 of 9
#> strongest over-represented (of 3):
#> Active -> Active z = +21.7 ***
#> Disengaged -> Disengaged z = +15.4 ***
#> Average -> Average z = +12.5 ***
#>
#> Initial states:
#> Active 0.382 ████████████████████████
#> Average 0.368 ███████████████████████
#> Disengaged 0.250 ████████████████
Fitting the tna object and fitting the original wide
data give the same LSA transition table.
isTRUE(all.equal(transitions(fit_from_tna), transitions(fit_wide),
check.attributes = FALSE))
#> [1] TRUE
Nestimate objects
Nestimate::build_network() uses the same long-log
grammar. The object it returns carries the prepared sequence data, so
lsa() can read it directly.
nestimate_fit <- Nestimate::build_network(
ai_long,
method = "tna",
actor = "project",
session = "session_id",
action = "code",
order = "order_in_session"
)
nestimate_fit
#> Transition Network (relative probabilities) [directed]
#> Weights: [0.004, 0.621] | mean: 0.125
#>
#> Weight matrix:
#> Ask Delegate Execute Explain Investigate Plan Repair Report
#> Ask 0.033 0.033 0.286 0.484 0.099 0.033 0.022 0.011
#> Delegate 0.007 0.025 0.182 0.050 0.093 0.621 0.014 0.007
#> Execute 0.010 0.022 0.510 0.055 0.247 0.116 0.030 0.010
#> Explain 0.020 0.016 0.256 0.131 0.313 0.069 0.083 0.111
#> Investigate 0.009 0.016 0.252 0.035 0.221 0.437 0.018 0.012
#> Plan 0.010 0.048 0.470 0.060 0.303 0.032 0.040 0.037
#> Repair 0.052 0.040 0.474 0.171 0.235 0.016 0.008 0.004
#> Report 0.006 0.062 0.377 0.111 0.284 0.031 0.086 0.043
#>
#> Initial probabilities:
#> Investigate 0.715 ████████████████████████████████████████
#> Delegate 0.185 ██████████
#> Execute 0.058 ███
#> Plan 0.035 ██
#> Ask 0.005
#> Explain 0.002
#> Repair 0.000
#> Report 0.000
fit_nestimate <- lsa(nestimate_fit)
fit_nestimate
#> 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 same raw log can also be fitted directly with the long-format
grammar. The Nestimate object and the direct
lsa() call recover the same transition table.
fit_ai_direct <- lsa(ai_long, actor = "project", session = "session_id",
action = "code", order = "order_in_session")
isTRUE(all.equal(transitions(fit_nestimate), transitions(fit_ai_direct),
check.attributes = FALSE))
#> [1] TRUE
Back to tna
Use lsa_to_tna() when the model is estimated with
lagdynamics and then handed to tna for
TNA-specific tooling.
tn <- lsa_to_tna(fit_wide, weights = "prob")
tn
#> State Labels :
#>
#> Active, Average, Disengaged
#>
#> Transition Probability Matrix :
#>
#> Active Average Disengaged
#> Active 0.698 0.267 0.035
#> Average 0.204 0.610 0.186
#> Disengaged 0.120 0.397 0.483
#>
#> Initial Probabilities :
#>
#> Active Average Disengaged
#> 0.382 0.368 0.250
tna::centralities(tn)
#> # A tibble: 3 × 10
#> state OutStrength InStrength ClosenessIn ClosenessOut Closeness Betweenness
#> * <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Active 0.302 0.324 0.0811 0.0779 0.100 0
#> 2 Average 0.390 0.664 0.160 0.0973 0.160 2
#> 3 Disenga… 0.517 0.221 0.0691 0.101 0.114 0
#> # ℹ 3 more variables: BetweennessRSP <dbl>, Diffusion <dbl>, Clustering <dbl>
The same quantities are also exposed without conversion: the
row-stochastic transition matrix and the initial-state distribution.
transition_probabilities(fit_long)
#> adapt cohesion consensus coregulate discuss emotion monitor
#> adapt 0.000000 0.2731 0.477 0.0216 0.0589 0.1198 0.0334
#> cohesion 0.002950 0.0271 0.498 0.1192 0.0596 0.1156 0.0330
#> consensus 0.004740 0.0149 0.082 0.1877 0.1880 0.0727 0.0466
#> coregulate 0.016244 0.0360 0.135 0.0234 0.2736 0.1721 0.0863
#> discuss 0.071374 0.0476 0.321 0.0843 0.1949 0.1058 0.0223
#> emotion 0.002467 0.3253 0.320 0.0342 0.1019 0.0768 0.0363
#> monitor 0.011165 0.0558 0.159 0.0579 0.3754 0.0907 0.0181
#> plan 0.000975 0.0252 0.290 0.0172 0.0679 0.1468 0.0755
#> synthesis 0.234663 0.0337 0.466 0.0445 0.0629 0.0706 0.0123
#> plan synthesis
#> adapt 0.0157 0.00000
#> cohesion 0.1410 0.00354
#> consensus 0.3958 0.00758
#> coregulate 0.2391 0.01878
#> discuss 0.0116 0.14098
#> emotion 0.0998 0.00282
#> monitor 0.2156 0.01605
#> plan 0.3742 0.00179
#> synthesis 0.0752 0.00000
initial(fit_long)
#> state init_prob
#> 1 adapt 0.0115
#> 2 cohesion 0.0605
#> 3 consensus 0.2140
#> 4 coregulate 0.0190
#> 5 discuss 0.1755
#> 6 emotion 0.1515
#> 7 monitor 0.1440
#> 8 plan 0.2045
#> 9 synthesis 0.0195
It also inherits cograph_network, so cograph-compatible
renderers can read the nodes and edges directly. The
probability-weighted network uses the TNA interpretation: edge weights
are conditional transition probabilities.
plot(fit_long, type = "network", weights = "tna")
Use the residual network when the claim is lag-sequential: which
transitions occur more or less often than expected under independence.
Use the probability/TNA network when the claim is descriptive: where the
process tends to go next.
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.