Run a multiverse based on a complete decision grid

Usage

run_multiverse(
  .grid,
  add_standardized = TRUE,
  save_model = FALSE,
  show_progress = TRUE
)

Arguments

.grid: a tibble produced by expand_decisions
add_standardized: logical. Whether to add standardized coefficients to the model output. Defaults to TRUE.
save_model: logical, indicates whether to save the model object in its entirety. The default is FALSE because model objects are usually large and under the hood, parameters and performance is used to summarize the most useful model information.
show_progress: logical, whether to show a progress bar while running.

Value

a single tibble containing tidied results for the model and any post-processing tests/tasks. For each unique test (e.g., an lm

or aov called on an lm), a list column with the function name is created with parameters and performance and any warnings or messages printed while fitting the models. Internally, modeling and post-processing functions are checked to see if there are tidy or glance methods available. If not, summary will be called instead.

Examples

library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess(process_name = "scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_preprocess(process_name = "scale_mod", mutate({mods} := scale({mods}))) |>
  add_model("no covariates",lm({dvs} ~ {ivs} * {mods})) |>
  add_model("covariate", lm({dvs} ~ {ivs} * {mods} + cov1)) |>
  add_postprocess("aov", aov())

pipeline_grid <- expand_decisions(full_pipeline)

# Run the whole multiverse
the_multiverse <- run_multiverse(pipeline_grid[1:10,])
#> Error in purrr::map(seq_len(nrow(.grid)), .progress = show_progress, function(x) {    multi_results <- list()    if ("models" %in% names(.grid)) {        multi_results$models <- run_universe_model(.grid = .grid,             decision_num = .grid$decision[x], add_standardized = add_standardized,             save_model = save_model)    }    purrr::reduce(multi_results, dplyr::left_join, by = "decision")}): ℹ In index: 1.
#> Caused by error in `map2()`:
#> ℹ In index: 1.
#> ℹ With name: model.
#> Caused by error:
#> ! object 'the_data' not found