Run your Pipeline
In multitool: Run Multiverse Style Analyses

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Once you have built your full specification blueprint and feel comfortable with how the pipeline is executed, you can implement a full multiverse-style analysis.

Simply use run_multiverse(<your expanded grid object>):

library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = 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)
  )

# 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))

# expand the pipeline
expanded_pipeline <- expand_decisions(full_pipeline)

# Run the multiverse
multiverse_results <- run_multiverse(expanded_pipeline)

multiverse_results

The result will be another tibble with various list columns.

It will always contain a list column named specifications containing all the information you generated in your blueprint. Next, there will a list column for your fitted model fitted, labelled model_fitted.

Unpacking a multiverse analysis

There are two main ways to unpack and examine multitool results. The first is by using tidyr::unnest().

Unnest

Inside the model_fitted column, multitool gives us 4 columns: model_parameters, model_performance, model_warnings, and model_messages.

multiverse_results |> unnest(model_fitted)

The model_parameters column gives you the result of calling parameters::parameters() on each model in your grid, which is a data.frame of model coefficients and their associated standard errors, confidence intervals, test statistic, and p-values.

multiverse_results |> 
  unnest(model_fitted) |> 
  unnest(model_parameters)

The model_performance column gives fit statistics, such as r^2^ or AIC and BIC values, computed by running performance::performance() on each model in your grid.

multiverse_results |> 
  unnest(model_fitted) |>
  unnest(model_performance)

The model_messages and model_warnings columns contain information provided by the modeling function. If something went wrong or you need to know something about a particular model, these columns will have captured messages and warnings printed by the modeling function.

Reveal

I wrote wrappers around the tidyr::unnest() workflow. The main function is reveal(). Pass a multiverse results object to reveal() and tell it which columns to grab by indicating the column name in the .what argument:

multiverse_results |> 
  reveal(.what = model_fitted)

If you want to get straight to a specific result you can specify a sub-list with the .which argument:

multiverse_results |> 
  reveal(.what = model_fitted, .which = model_parameters)

`reveal_model_*`

multitool will run and save anything you put in your pipeline but most often, you will want to look at model parameters and/or performance. To that end, there are a set of convenience functions for getting at the most common multiverse results: reveal_model_parameters, reveal_model_performance, reveal_model_messages, and reveal_model_warnings.

reveal_model_parameters unpacks the model parameters in your multiverse:

multiverse_results |> 
  reveal_model_parameters()

reveal_model_performance unpacks the model performance:

multiverse_results |> 
  reveal_model_performance()

Unpacking Specifications

You can also choose to expand your decision grid with .unpack_specs to see which decisions produced what result. You have two options for unpacking your decisions - wide or long. If you set .unpack_specs = 'wide', you get one column per decion variable. This is exactly the same as how your decisions appeared in your grid.

multiverse_results |> 
  reveal_model_parameters(.unpack_specs = "wide")

If you set .unpack_specs = 'long', your decisions get stacked into two columns: decision_set and alternatives. This format is nice for plotting a particular result from a multiverse analyses per different decision alternatives.

multiverse_results |> 
  reveal_model_performance(.unpack_specs = "long")

Condense

Unpacking specifications alongside specific results allows us to examine the effects of our pipeline decisions.

A powerful way to organize these results is to summarize a specific results column, say the r^2^ values of our model over the entire multiverse. condense() takes a result column and summarizes it with the .how argument, which takes a list in the form of list(<a name you pick> = <summary function>).

.how will create a column named like so <column being condsensed>_<summary function name provided>. For this case, we have r2_mean and r2_median.

# model performance r2 summaries
multiverse_results |>
  reveal_model_performance() |> 
  condense(r2, list(mean = mean, median = median))

# model parameters for our predictor of interest
multiverse_results |>
  reveal_model_parameters() |> 
  filter(str_detect(parameter, "iv")) |>
  condense(coefficient, list(mean = mean, median = median))

In the last example, we have filtered our multiverse results to look at our predictors iv* to see what the mean and median effect was (over all combinations of decisions) on our outcomes.

However, we had three versions of our predictor and two outcomes, so combining dplyr::group_by() with condense() might be more informative:

multiverse_results |>
  reveal_model_parameters(.unpack_specs = "wide") |> 
  filter(str_detect(parameter, "iv")) |>
  group_by(ivs, dvs) |>
  condense(coefficient, list(mean = mean, median = median))

If we were interested in all the terms of the model, we can leverage group_by further:

multiverse_results |>
  reveal_model_parameters(.unpack_specs = "wide") |> 
  group_by(parameter, dvs) |>
  condense(coefficient, list(mean = mean, median = median))