Reveal the model parameters of a multiverse analysis

Usage

reveal_model_parameters(.multi, effect_key = NULL, .unpack_specs = "no")

Arguments

.multi: a multiverse list-column tibble produced by run_multiverse.
effect_key: character, if you added parameter keys to your pipeline, you can specify if you would like filter the parameters using one of your parameter keys. This is useful when different variables are being switched out across the multiverse but represent the same effect of interest.
.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 model paramerters from the multiverse.

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_parameters()
#> Error: object 'the_multiverse' not found