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Worked examples

Source: vignettes/worked-examples.Rmd
worked-examples.Rmd

pilotr ships one ready-to-run specification per design family. The same JSON files drive the Python twin and the no-code app, so a design authored once runs unchanged across all three. pilotr_example() lists them, and returns the path to each for load_spec().

pilotr_example()
#> [1] "beta_proportion"         "between_2group_gaussian"
#> [3] "crossed_mixed_rt"        "nested_clusters"        
#> [5] "ordinal_likert_between"  "partial_crossing"       
#> [7] "poisson_counts_between"  "reading_time_continuous"

Each specification is simulated below, showing the family it draws from and the first rows of the data it produces. The specification format itself is covered in the Get started article.

desc <- c(
  between_2group_gaussian = "Two-group between-subjects Gaussian.",
  crossed_mixed_rt        = "Crossed by-subject and by-item reaction times, shifted lognormal.",
  beta_proportion         = "Bounded proportions through the Beta family.",
  ordinal_likert_between  = "Five-point Likert responses via a cumulative-logit model.",
  poisson_counts_between  = "Count outcomes through a log link.",
  reading_time_continuous = "A continuous predictor with a lognormal reading-time outcome.",
  nested_clusters         = "Subjects nested in higher-level clusters, an extra grouping factor.",
  partial_crossing        = "Each subject sees a sampled subset of items."
)

for (name in pilotr_example()) {
  spec <- load_spec(pilotr_example(name))
  d <- simulate_design(spec)
  blurb <- if (name %in% names(desc)) desc[[name]] else ""
  cat(sprintf("\n### %s\n\n", name))
  cat(sprintf("%s The `%s` family, %d rows.\n\n",
              blurb, spec$response$family, nrow(d)))
  # These tables are printed data, so they take the site's code size rather than the prose
  # size a pipe table would inherit. The class is what the stylesheet keys on, and
  # table.attr reaches the output only for format = "html"; pipe output discards it.
  cat(knitr::kable(head(d, 4), format = "html",
                   table.attr = 'class="table data-output"'), sep = "\n")
  cat("\n\n")
}

beta_proportion

Bounded proportions through the Beta family. The beta family, 200 rows.

subject group prop
1 control 0.29928
2 control 0.54044
3 control 0.25637
4 control 0.33684

between_2group_gaussian

Two-group between-subjects Gaussian. The gaussian family, 64 rows.

subject group score
1 control 95.7157
2 control 90.0921
3 control 119.1958
4 control 86.4388

crossed_mixed_rt

Crossed by-subject and by-item reaction times, shifted lognormal. The shifted_lognormal family, 1440 rows.

subject item condition RT
1 1 related 668.6564
1 1 unrelated 727.3870
1 2 related 699.1907
1 2 unrelated 502.9760

nested_clusters

Subjects nested in higher-level clusters, an extra grouping factor. The gaussian family, 4800 rows.

subject item site condition y
1 1 1 a 0.16487
1 1 1 b 2.11132
1 2 1 a 0.72468
1 2 1 b 1.70270

ordinal_likert_between

Five-point Likert responses via a cumulative-logit model. The ordinal family, 400 rows.

subject group rating
1 control 2
2 control 4
3 control 3
4 control 3

partial_crossing

Each subject sees a sampled subset of items. The gaussian family, 1440 rows.

subject item condition rt
1 3 a 393.4013
1 3 b 532.2501
1 5 a 524.1783
1 5 b 632.9765

poisson_counts_between

Count outcomes through a log link. The poisson family, 2000 rows.

subject group count
1 control 3
2 control 5
3 control 3
4 control 9

reading_time_continuous

A continuous predictor with a lognormal reading-time outcome. The lognormal family, 4000 rows.

subject item narration SyntaxPC CoherencePC age reading_time_per_word
1 1 off -1.739797 -0.0215117 0.0632888 0.37485
1 1 on -1.739797 -0.0215117 0.0632888 0.32765
1 2 off -1.891749 0.7268579 0.0632888 0.15592
1 2 on -1.891749 0.7268579 0.0632888 0.23166