multitool (version 0.1.5)

condense: Summarize multiverse parameters

Description

Summarize multiverse parameters

Usage

condense(.unpacked, .what, .how, .group = NULL, list_cols = TRUE)

Value

a summarized tibble containing a column for each summary method from .how

Arguments

.unpacked

an unpacked (with reveal or tidyr::unnest) multiverse dataset.

.what

a specific column to summarize. This could be a model estimate, a summary statistic, correlation, or any other estimate computed over the multiverse.

.how

a named list. The list should contain summary functions (e.g., mean or median) the user would like to compute over the individual estimates from the multiverse

.group

an optional variable to group the results. This argument is passed directly to the .by argument used in dplyr::across

list_cols

logical, whether to create list columns for the raw values of any summarized columns. Useful for creating visualizations and tables. Default is TRUE.

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,])

# Reveal and condense
the_multiverse |>
  reveal_model_parameters() |>
  filter(str_detect(parameter, "iv")) |>
  condense(unstd_coef, list(mean = mean, median = median))