Plan · Analyze · Explore
Install from CRAN:
install.packages("multitool")You can install the development version of multitool from
GitHub with:
# install.packages("devtools")
devtools::install_github("ethan-young/multitool")The goal of multitool is to provide a set of tools for designing and
running multiverse-style analyses. I designed it to help users create an
incremental workflow for slowly building up, keeping track of, and
unpacking multiverse analyses and results.
For those unfamiliar with multiverse analysis, here is a short primer:
<iframe width="100%" height="600" src="multiverse-primer/multiverse-primer.html" style="border: 2px #dee2e6 solid; border-radius: 5px;"> </iframe>I designed multitool to do multiverse analysis but its really just a
tool for exploration.
In any new field, area, or project, there is a lot of uncertainty about
which data analysis decisions to make. Clear research questions and
criteria help reduce uncertainty about how to answer them but they never
fully reduce them. multitool helps organize and systematically explore
different options. That’s really it.
I designed multitool to help users take a single use case (e.g., a
single analysis pipeline) and expand it into a workflow to include
alternative versions of the same analysis.
For example, imagine you would like to take some data, remove outliers,
transform variables, run a linear model, do a post-hoc analysis, and
plot the results. multitool can take theses tasks and transform them
into a blueprint, which provides instructions for running your analysis
pipeline.
The functions were designed to play nice with the
tidyverse and require using the base R
pipe |>. This makes it easy to quickly convert a single analysis into
a multiverse analysis.
My vision of a multitool workflow contains five steps:
multitool cannot make decisions for you but – once you know your set
of data decisions – it can help you create and organize them into the
workflow above.
A defining feature of multitool is that it saves your code. This
allows the user to grab the code that produces a result and inspect it
for accuracy, errors, or simply for peace of mind. By quickly grabbing
code, the user can iterate between creating their blueprint and checking
that the code works as intended.
multitool allows the user to model data however they’d like. The user
is responsible for loading the relevant modeling packages. Regardless of
your model choice, multitool will capture your code and build a
blueprint with alternative analysis pipelines.
Finally, multiverse analyses were originally intended to look at how model parameters shift as a function of arbitrary data decisions. However, any computation might change depending on how you slice and dice the data. For this reason, I also built functions for computing descriptive, correlation, and reliability analysis alongside a particular modelling pipeline.
# load packages
library(tidyverse)
library(multitool)
# create some data
the_data <-
data.frame(
id = 1:100,
iv1 = rnorm(100),
iv2 = rnorm(100),
iv3 = rnorm(100),
mod = rnorm(100),
dv1 = rnorm(100),
dv2 = rnorm(100),
include1 = rbinom(100, size = 1, prob = .1),
include2 = sample(1:3, size = 100, replace = TRUE),
include3 = rnorm(100)
)
# create a pipeline blueprint
full_pipeline <-
the_data |>
add_filters(include1 == 0, include2 != 3, include3 > -2.5) |>
add_variables(var_group = "ivs", iv1, iv2, iv3) |>
add_variables(var_group = "dvs", dv1, dv2) |>
add_model("linear model", lm({dvs} ~ {ivs} * mod))
full_pipeline
#> # A tibble: 12 × 3
#> type group code
#> <chr> <chr> <chr>
#> 1 filters include1 include1 == 0
#> 2 filters include1 include1 %in% unique(include1)
#> 3 filters include2 include2 != 3
#> 4 filters include2 include2 %in% unique(include2)
#> 5 filters include3 include3 > -2.5
#> 6 filters include3 include3 %in% unique(include3)
#> 7 variables ivs iv1
#> 8 variables ivs iv2
#> 9 variables ivs iv3
#> 10 variables dvs dv1
#> 11 variables dvs dv2
#> 12 models linear model lm({dvs} ~ {ivs} * mod)
# Expand your blueprint into a grid
expanded_pipeline <- expand_decisions(full_pipeline)
expanded_pipeline
#> # A tibble: 48 × 4
#> decision variables filters models
#> <chr> <list> <list> <list>
#> 1 1 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> 2 2 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> 3 3 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> 4 4 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> 5 5 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> 6 6 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> 7 7 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> 8 8 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> 9 9 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> 10 10 <tibble [1 × 2]> <tibble [1 × 3]> <tibble [1 × 2]>
#> # ℹ 38 more rows
# Run the blueprint
multiverse_results <- run_multiverse(expanded_pipeline)
multiverse_results
#> # A tibble: 48 × 4
#> decision specifications model_fitted pipeline_code
#> <chr> <list> <list> <list>
#> 1 1 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> 2 2 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> 3 3 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> 4 4 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> 5 5 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> 6 6 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> 7 7 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> 8 8 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> 9 9 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> 10 10 <tibble [1 × 3]> <tibble [1 × 5]> <tibble [1 × 2]>
#> # ℹ 38 more rows
# Unpack model coefficients
multiverse_results |>
reveal_model_parameters()
#> # A tibble: 192 × 16
#> decision specifications model_function parameter coefficient se ci
#> <chr> <list> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 1 <tibble [1 × 3]> lm (Intercept) -0.0597 0.121 0.95
#> 2 1 <tibble [1 × 3]> lm iv1 -0.140 0.130 0.95
#> 3 1 <tibble [1 × 3]> lm mod 0.0693 0.136 0.95
#> 4 1 <tibble [1 × 3]> lm iv1:mod 0.0228 0.177 0.95
#> 5 2 <tibble [1 × 3]> lm (Intercept) 0.145 0.167 0.95
#> 6 2 <tibble [1 × 3]> lm iv1 0.0458 0.179 0.95
#> 7 2 <tibble [1 × 3]> lm mod -0.253 0.187 0.95
#> 8 2 <tibble [1 × 3]> lm iv1:mod 0.0647 0.243 0.95
#> 9 3 <tibble [1 × 3]> lm (Intercept) -0.0954 0.125 0.95
#> 10 3 <tibble [1 × 3]> lm iv2 -0.147 0.126 0.95
#> # ℹ 182 more rows
#> # ℹ 9 more variables: ci_low <dbl>, ci_high <dbl>, t <dbl>, df_error <int>,
#> # p <dbl>, model_performance <list>, model_warnings <list>,
#> # model_messages <list>, pipeline_code <list>
# Unpack model fit statistics
multiverse_results |>
reveal_model_performance()
#> # A tibble: 48 × 14
#> decision specifications model_function model_parameters aic aicc bic
#> <chr> <list> <chr> <list> <dbl> <dbl> <dbl>
#> 1 1 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 153. 154. 163.
#> 2 2 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 189. 190. 199.
#> 3 3 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 152. 153. 162.
#> 4 4 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 186. 187. 196.
#> 5 5 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 154. 155. 164.
#> 6 6 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 188. 189. 198.
#> 7 7 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 153. 154. 163.
#> 8 8 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 189. 190. 199.
#> 9 9 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 152. 153. 162.
#> 10 10 <tibble [1 × 3]> lm <prmtrs_m [4 × 9]> 186. 187. 196.
#> # ℹ 38 more rows
#> # ℹ 7 more variables: r2 <dbl>, r2_adjusted <dbl>, rmse <dbl>, sigma <dbl>,
#> # model_warnings <list>, model_messages <list>, pipeline_code <list>
# Summarize model coefficients
multiverse_results |>
reveal_model_parameters() |>
group_by(parameter) |>
condense(coefficient, list(mean = mean, median = median, sd = sd))
#> # A tibble: 8 × 5
#> parameter coefficient_mean coefficient_median coefficient_sd coefficient_list
#> <chr> <dbl> <dbl> <dbl> <list>
#> 1 (Intercep… 0.0223 0.0307 0.0834 <dbl [48]>
#> 2 iv1 -0.121 -0.140 0.111 <dbl [16]>
#> 3 iv1:mod 0.114 0.124 0.0785 <dbl [16]>
#> 4 iv2 -0.171 -0.168 0.0613 <dbl [16]>
#> 5 iv2:mod 0.0851 0.0958 0.0707 <dbl [16]>
#> 6 iv3 0.0779 0.104 0.0589 <dbl [16]>
#> 7 iv3:mod 0.0405 0.0284 0.0606 <dbl [16]>
#> 8 mod -0.0507 -0.0174 0.119 <dbl [48]>
# Summarize fit statistics
multiverse_results |>
reveal_model_performance() |>
condense(r2, list(mean = mean, sd = sd))
#> # A tibble: 1 × 3
#> r2_mean r2_sd r2_list
#> <dbl> <dbl> <list>
#> 1 0.0370 0.0228 <dbl [48]>