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From raw data to model with tidyILD
tidyILD authors
2026-03-05
This vignette walks through the full tidyILD pipeline: prepare data,
inspect structure, apply within-between decomposition and lags, fit a
mixed-effects model, and run diagnostics and plots.
Simulate and prepare
library(tidyILD)
# Simulate simple ILD
d <- ild_simulate(n_id = 10, n_obs_per = 12, irregular = TRUE, seed = 42)
# Prepare: encode time structure and add .ild_* columns
x <- ild_prepare(d, id = "id", time = "time", gap_threshold = 7200)
Inspect
ild_summary(x)
#> $summary
#> # A tibble: 1 × 7
#> n_id n_obs time_min time_max prop_gap median_dt_sec iqr_dt_sec
#> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 10 120 0 40136. 0 3612. 640.
#>
#> $n_units
#> [1] 10
#>
#> $n_obs
#> [1] 120
#>
#> $time_range
#> [1] 0 40136
#>
#> $spacing
#> $spacing$median_dt
#> [1] 3612.244
#>
#> $spacing$iqr_dt
#> [1] 640.2958
#>
#> $spacing$n_intervals
#> [1] 110
#>
#> $spacing$pct_gap
#> [1] 0
#>
#> $spacing$by_id
#> # A tibble: 10 × 5
#> id median_dt iqr_dt n_intervals pct_gap
#> <int> <dbl> <dbl> <int> <dbl>
#> 1 1 3627. 549. 11 0
#> 2 2 3702. 854. 11 0
#> 3 3 3617. 740. 11 0
#> 4 4 3321. 702. 11 0
#> 5 5 3616. 588. 11 0
#> 6 6 3438. 481. 11 0
#> 7 7 3767. 816. 11 0
#> 8 8 3591. 499. 11 0
#> 9 9 3609. 194. 11 0
#> 10 10 3700. 521. 11 0
#>
#>
#> $n_gaps
#> [1] 0
#>
#> $pct_gap
#> [1] 0
ild_spacing_class(x)
#> [1] "regular-ish"
Within-person centering and lags
x <- ild_center(x, y)
x <- ild_lag(x, y, mode = "gap_aware", max_gap = 7200)
Fit a model
Without residual autocorrelation (lmer):
fit0 <- ild_lme(y ~ 1 + (1 | id), data = x, ar1 = FALSE, warn_no_ar1 = FALSE)
With AR1 residual correlation (nlme):
fit1 <- ild_lme(y ~ 1, data = x, ar1 = TRUE, correlation_class = "CAR1")
Diagnostics and plots
diag <- ild_diagnostics(fit1, data = x)
names(diag) # meta, data, stats
#> [1] "meta" "data" "stats"
names(plot_ild_diagnostics(diag)) # plot names for requested types
#> [1] "residual_acf" "residuals_vs_fitted" "residuals_vs_time"
#> [4] "qq"
# Pooled residual ACF (tibble)
head(diag$stats$acf$pooled)
#> # A tibble: 6 × 2
#> lag acf
#> <dbl> <dbl>
#> 1 0 1
#> 2 1 0.121
#> 3 2 -0.00832
#> 4 3 -0.113
#> 5 4 -0.0886
#> 6 5 -0.147
# By-id ACF when by_id = TRUE: one tibble per person
names(diag$stats$acf$by_id)
#> [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
head(diag$stats$acf$by_id[[1]])
#> # A tibble: 6 × 2
#> lag acf
#> <dbl> <dbl>
#> 1 0 1
#> 2 1 0.361
#> 3 2 -0.0851
#> 4 3 0.149
#> 5 4 0.196
#> 6 5 0.0752
ild_plot(x, type = "trajectory", var = "y", max_ids = 5)
ild_plot(fit1, type = "fitted")
Reproducibility
Use a fixed seed when simulating or fitting models so results can be
recreated. The pipeline is deterministic for a given seed and data. When
saving results (e.g. after [ild_lme()] or [ild_diagnostics()]), you can
attach a reproducibility manifest and save a single bundle with
[ild_manifest()] and [ild_bundle()]:
# Optional: build a manifest with scenario and seed, then bundle the fit for saving
manifest <- ild_manifest(seed = 42, scenario = ild_summary(x), include_session = FALSE)
bundle <- ild_bundle(fit1, manifest = manifest, label = "model_ar1")
# saveRDS(bundle, "run.rds") # one file with result + manifest + label
names(bundle)
#> [1] "result" "manifest" "label"
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.
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