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Bootstrap Confidence Intervals for an LSA Fit

Source: R/bootstrap_lsa.R
bootstrap_lsa.Rd

Non-parametric bootstrap for any LSA fit produced by lsa(). Resamples the underlying sequence data (whole sequences when more than one is available; geometric-block stationary bootstrap on events otherwise), refits the engine on each resample using the immutable recipe stored in fit$params, and aggregates per-edge statistics into a tidy data frame.

Usage

bootstrap_lsa(
  fit,
  R = 1000L,
  level = c("auto", "sequence", "event"),
  block_length = NULL,
  level_alpha = 0.95,
  indices = NULL,
  parallel = FALSE,
  n_cores = NULL,
  verbose = FALSE,
  ...
)

Arguments

fit

An lsa object returned by lsa().

R

Integer. Number of bootstrap replicates. Default 1000.

level

Character. Resampling unit: "sequence" (resample whole sequences with replacement, default when fit has more than one sequence), "event" (stationary block bootstrap on the event stream, used automatically for single-sequence input), or "auto" (default; pick based on fit$data$n_sequences).

block_length

For event-level bootstrap, mean geometric block length. Default NULL -> ceiling(sqrt(T)).

level_alpha

Numeric. Confidence level for percentile intervals. Default 0.95.

indices

Optional integer matrix of replay indices, one row per resample (row b is used for resample b). For sequence-level bootstrap it must be R x S with each entry a sequence index in 1..S. For event-level bootstrap it is R x T with each entry an event position in 1..T (the fully expanded positions, not block starts). When supplied, replaces internal RNG and enables bit-identical reproducibility across runs. Dimensions and ranges are validated. See Details.

parallel

Logical. Use multi-core resampling. Default FALSE. Requires base R only (parallel package).

n_cores

Integer. Worker count when parallel = TRUE. Default NULL -> parallel::detectCores() - 1.

verbose

Logical. Print progress every 100 replicates. Default FALSE.

...

Reserved for future use.

Value

An object of class c("lsa_bootstrap", "list") with:

edges

Tidy per-edge data frame with observed + bootstrap mean, se, ci_low, ci_high, p_boot, and stable for count, adj_res, prob, and yules_q.

boot_obs

R x K^2 numeric matrix: cell-wise observed count from each replicate (flattened in as.vector(obs) order).

boot_adj_res

R x K^2 matrix of adjusted residuals.

R, level, level_alpha, indices_used

Recipe metadata.

fit

Reference to the original fit (for $params / labels).

Details

Sequence-level resampling (default for multi-sequence input). Each resample draws S sequence indices with replacement from seq_len(S) and rebuilds the multi-sequence input as the corresponding list of event vectors. Preserves within-sequence structure.

Event-level resampling (single-sequence input). Implements the stationary block bootstrap of Politis & Romano (1994). Block length is geometric with mean block_length; resampled blocks wrap around the event stream and are concatenated until total length equals the original T.

Reproducibility hook. Supply indices as an R x S integer matrix of sequence indices (sequence-level) or an R x T matrix of event positions (event-level) to deterministically replay the bootstrap across sessions, processes, or languages. The event-level matrix holds the fully expanded resampled positions, i.e. the same form produced internally, so a captured indices_used can be fed straight back in.

NA handling. Per-cell summary statistics (mean, se, ci_low, ci_high, p_boot) are computed with na.rm = TRUE, so replicates that produced NA for a given cell (for example structural-zero cells, or cells whose row marginal collapsed to zero in the resampled data) are excluded from that cell's summary. The summary therefore reflects only the finite replicates; cells whose every replicate was NA come back as NA themselves.

References

Efron, B. (1979). Bootstrap methods: another look at the jackknife. Annals of Statistics, 7(1), 1-26.

Politis, D. N., & Romano, J. P. (1994). The stationary bootstrap. Journal of the American Statistical Association, 89(428), 1303-1313.

See also

permute_lsa(), stability_lsa()

Examples

# \donttest{
fit <- lsa(engagement, engine = "classical")
bs <- bootstrap_lsa(fit, R = 200)
head(bs$edges)
#>         from      to observed count_mean  count_se count_ci_low count_ci_high
#> 1     Active  Active      459    469.300 52.308997      376.975       569.150
#> 2    Average  Active      153    152.005 16.286844      122.000       186.025
#> 3 Disengaged  Active       39     38.315  6.084479       27.975        49.000
#> 4     Active Average      176    175.075 17.506621      141.875       213.025
#> 5    Average Average      458    450.865 42.901509      369.875       538.050
#> 6 Disengaged Average      129    127.660 14.242847      101.975       154.025
#>   adj_res_observed adj_res_mean adj_res_se adj_res_ci_low adj_res_ci_high
#> 1        21.662811     21.83015   1.521667      18.806855      24.6240543
#> 2       -12.906038    -12.94027   1.496602     -15.530175     -10.0943842
#> 3       -10.549486    -10.77165   1.218591     -12.757864      -8.3070225
#> 4       -11.319132    -11.37662   1.572194     -14.382033      -8.0782759
#> 5        12.452615     12.52827   1.402022       9.788089      15.0397178
#> 6        -1.736461     -1.66657   1.468575      -4.454881       0.9783829
#>   adj_res_p_boot adj_res_stable prob_observed prob_mean prob_ci_low
#> 1           0.00           TRUE     0.6975684 0.7023435  0.65231852
#> 2           0.00           TRUE     0.2037284 0.2057721  0.16464788
#> 3           0.00           TRUE     0.1200000 0.1188032  0.08244323
#> 4           0.00           TRUE     0.2674772 0.2634867  0.20960424
#> 5           0.00           TRUE     0.6098535 0.6073409  0.54974454
#> 6           0.25          FALSE     0.3969231 0.3943909  0.31606167
#>   prob_ci_high yules_q_observed yules_q_mean yules_q_ci_low yules_q_ci_high
#> 1    0.7555365        0.8278779    0.8295468      0.7604949      0.87912442
#> 2    0.2549351       -0.6010580   -0.6015555     -0.6952226     -0.49358512
#> 3    0.1605894       -0.6983917   -0.7068677     -0.8023661     -0.58578503
#> 4    0.3158819       -0.5335259   -0.5347029     -0.6436904     -0.40695051
#> 5    0.6525239        0.5530510    0.5557749      0.4484348      0.64459557
#> 6    0.4722248       -0.1083175   -0.1028465     -0.2764733      0.06399258
# }