Reveal the contents of a multiverse analysis

Usage

reveal(.multi, .what, .which = NULL, .unpack_specs = "no")

Arguments

.multi: a multiverse list-column tibble produced by run_multiverse.
.what: the name of a list-column you would like to unpack
.which: any sub-list columns you would like to unpack
.unpack_specs: character, options are "no", "wide", or "long". "no" (default) keeps specifications in a list column, wide unnests specifications with each specification category as a column. "long" unnests specifications and stacks them into long format, which stacks specifications into a decision_set and alternatives columns. This is mainly useful for plotting.

Value

the unnested part of the multiverse requested. This usually contains the particular estimates or statistics you would like to analyze over the decision grid specified.

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_model("linear_model", lm({dvs} ~ {ivs} * {mods} + cov1))

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

# Reveal results of the linear model
the_multiverse |> reveal(model_fitted, model_parameters)
#> Error: object 'the_multiverse' not found