Run your Pipeline • multitool

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
#> # 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

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)
#> # A tibble: 48 × 8
#>    decision specifications   model_function model_parameters model_performance 
#>    <chr>    <list>           <chr>          <list>           <list>            
#>  1 1        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  2 2        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  3 3        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  4 4        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  5 5        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  6 6        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  7 7        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  8 8        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  9 9        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#> 10 10       <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#> # ℹ 38 more rows
#> # ℹ 3 more variables: model_warnings <list>, model_messages <list>,
#> #   pipeline_code <list>

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)
#> # A tibble: 192 × 20
#>    decision specifications   model_function parameter unstd_coef     se unstd_ci
#>    <chr>    <list>           <chr>          <chr>          <dbl>  <dbl>    <dbl>
#>  1 1        <tibble [1 × 3]> lm             (Interce… -0.000228  0.0571     0.95
#>  2 1        <tibble [1 × 3]> lm             iv1        0.0152    0.0592     0.95
#>  3 1        <tibble [1 × 3]> lm             mod        0.0139    0.0582     0.95
#>  4 1        <tibble [1 × 3]> lm             iv1:mod   -0.0516    0.0571     0.95
#>  5 2        <tibble [1 × 3]> lm             (Interce… -0.0452    0.0587     0.95
#>  6 2        <tibble [1 × 3]> lm             iv1        0.0315    0.0609     0.95
#>  7 2        <tibble [1 × 3]> lm             mod        0.0470    0.0599     0.95
#>  8 2        <tibble [1 × 3]> lm             iv1:mod    0.0337    0.0587     0.95
#>  9 3        <tibble [1 × 3]> lm             (Interce…  0.00239   0.0567     0.95
#> 10 3        <tibble [1 × 3]> lm             iv2       -0.0000862 0.0584     0.95
#> # ℹ 182 more rows
#> # ℹ 13 more variables: unstd_ci_low <dbl>, unstd_ci_high <dbl>, t <dbl>,
#> #   df_error <int>, p <dbl>, std_coef <dbl>, std_ci <dbl>, std_ci_low <dbl>,
#> #   std_ci_high <dbl>, model_performance <list>, model_warnings <list>,
#> #   model_messages <list>, pipeline_code <list>

The model_performance column gives fit statistics, such as r² or AIC and BIC values, computed by running performance::performance() on each model in your grid.

multiverse_results |> 
  unnest(model_fitted) |>
  unnest(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>        907.  907.  925.
#>  2 2        <tibble [1 × 3]> lm             <prmtrs_m>        925.  925.  944.
#>  3 3        <tibble [1 × 3]> lm             <prmtrs_m>        904.  905.  923.
#>  4 4        <tibble [1 × 3]> lm             <prmtrs_m>        925.  925.  944.
#>  5 5        <tibble [1 × 3]> lm             <prmtrs_m>        904.  904.  923.
#>  6 6        <tibble [1 × 3]> lm             <prmtrs_m>        925.  925.  944.
#>  7 7        <tibble [1 × 3]> lm             <prmtrs_m>        911.  911.  930.
#>  8 8        <tibble [1 × 3]> lm             <prmtrs_m>        930.  930.  948.
#>  9 9        <tibble [1 × 3]> lm             <prmtrs_m>        908.  909.  927.
#> 10 10       <tibble [1 × 3]> lm             <prmtrs_m>        930.  930.  949.
#> # 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>

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)
#> # A tibble: 48 × 8
#>    decision specifications   model_function model_parameters model_performance 
#>    <chr>    <list>           <chr>          <list>           <list>            
#>  1 1        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  2 2        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  3 3        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  4 4        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  5 5        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  6 6        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  7 7        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  8 8        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#>  9 9        <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#> 10 10       <tibble [1 × 3]> lm             <prmtrs_m>       <prfrmnc_ [1 × 7]>
#> # ℹ 38 more rows
#> # ℹ 3 more variables: model_warnings <list>, model_messages <list>,
#> #   pipeline_code <list>

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)
#> # A tibble: 192 × 20
#>    decision specifications   model_function parameter unstd_coef     se unstd_ci
#>    <chr>    <list>           <chr>          <chr>          <dbl>  <dbl>    <dbl>
#>  1 1        <tibble [1 × 3]> lm             (Interce… -0.000228  0.0571     0.95
#>  2 1        <tibble [1 × 3]> lm             iv1        0.0152    0.0592     0.95
#>  3 1        <tibble [1 × 3]> lm             mod        0.0139    0.0582     0.95
#>  4 1        <tibble [1 × 3]> lm             iv1:mod   -0.0516    0.0571     0.95
#>  5 2        <tibble [1 × 3]> lm             (Interce… -0.0452    0.0587     0.95
#>  6 2        <tibble [1 × 3]> lm             iv1        0.0315    0.0609     0.95
#>  7 2        <tibble [1 × 3]> lm             mod        0.0470    0.0599     0.95
#>  8 2        <tibble [1 × 3]> lm             iv1:mod    0.0337    0.0587     0.95
#>  9 3        <tibble [1 × 3]> lm             (Interce…  0.00239   0.0567     0.95
#> 10 3        <tibble [1 × 3]> lm             iv2       -0.0000862 0.0584     0.95
#> # ℹ 182 more rows
#> # ℹ 13 more variables: unstd_ci_low <dbl>, unstd_ci_high <dbl>, t <dbl>,
#> #   df_error <int>, p <dbl>, std_coef <dbl>, std_ci <dbl>, std_ci_low <dbl>,
#> #   std_ci_high <dbl>, model_performance <list>, model_warnings <list>,
#> #   model_messages <list>, pipeline_code <list>

`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()
#> # A tibble: 192 × 20
#>    decision specifications   model_function parameter unstd_coef     se unstd_ci
#>    <chr>    <list>           <chr>          <chr>          <dbl>  <dbl>    <dbl>
#>  1 1        <tibble [1 × 3]> lm             (Interce… -0.000228  0.0571     0.95
#>  2 1        <tibble [1 × 3]> lm             iv1        0.0152    0.0592     0.95
#>  3 1        <tibble [1 × 3]> lm             mod        0.0139    0.0582     0.95
#>  4 1        <tibble [1 × 3]> lm             iv1:mod   -0.0516    0.0571     0.95
#>  5 2        <tibble [1 × 3]> lm             (Interce… -0.0452    0.0587     0.95
#>  6 2        <tibble [1 × 3]> lm             iv1        0.0315    0.0609     0.95
#>  7 2        <tibble [1 × 3]> lm             mod        0.0470    0.0599     0.95
#>  8 2        <tibble [1 × 3]> lm             iv1:mod    0.0337    0.0587     0.95
#>  9 3        <tibble [1 × 3]> lm             (Interce…  0.00239   0.0567     0.95
#> 10 3        <tibble [1 × 3]> lm             iv2       -0.0000862 0.0584     0.95
#> # ℹ 182 more rows
#> # ℹ 13 more variables: unstd_ci_low <dbl>, unstd_ci_high <dbl>, t <dbl>,
#> #   df_error <int>, p <dbl>, std_coef <dbl>, std_ci <dbl>, std_ci_low <dbl>,
#> #   std_ci_high <dbl>, model_performance <list>, model_warnings <list>,
#> #   model_messages <list>, pipeline_code <list>

reveal_model_performance unpacks the model performance:

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>        907.  907.  925.
#>  2 2        <tibble [1 × 3]> lm             <prmtrs_m>        925.  925.  944.
#>  3 3        <tibble [1 × 3]> lm             <prmtrs_m>        904.  905.  923.
#>  4 4        <tibble [1 × 3]> lm             <prmtrs_m>        925.  925.  944.
#>  5 5        <tibble [1 × 3]> lm             <prmtrs_m>        904.  904.  923.
#>  6 6        <tibble [1 × 3]> lm             <prmtrs_m>        925.  925.  944.
#>  7 7        <tibble [1 × 3]> lm             <prmtrs_m>        911.  911.  930.
#>  8 8        <tibble [1 × 3]> lm             <prmtrs_m>        930.  930.  948.
#>  9 9        <tibble [1 × 3]> lm             <prmtrs_m>        908.  909.  927.
#> 10 10       <tibble [1 × 3]> lm             <prmtrs_m>        930.  930.  949.
#> # 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>

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 decision variable. This is exactly the same as how your decisions appeared in your grid.

multiverse_results |> 
  reveal_model_parameters(.unpack_specs = "wide")
#> # A tibble: 192 × 26
#>    decision ivs   dvs   include1      include2      include3    model model_meta
#>    <chr>    <chr> <chr> <chr>         <chr>         <chr>       <chr> <chr>     
#>  1 1        iv1   dv1   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#>  2 1        iv1   dv1   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#>  3 1        iv1   dv1   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#>  4 1        iv1   dv1   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#>  5 2        iv1   dv2   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#>  6 2        iv1   dv2   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#>  7 2        iv1   dv2   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#>  8 2        iv1   dv2   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#>  9 3        iv2   dv1   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#> 10 3        iv2   dv1   include1 == 0 include2 != 3 include3 >… lm(d… linear mo…
#> # ℹ 182 more rows
#> # ℹ 18 more variables: model_function <chr>, parameter <chr>, unstd_coef <dbl>,
#> #   se <dbl>, unstd_ci <dbl>, unstd_ci_low <dbl>, unstd_ci_high <dbl>, t <dbl>,
#> #   df_error <int>, p <dbl>, std_coef <dbl>, std_ci <dbl>, std_ci_low <dbl>,
#> #   std_ci_high <dbl>, model_performance <list>, model_warnings <list>,
#> #   model_messages <list>, pipeline_code <list>

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")
#> # A tibble: 288 × 15
#>    decision decision_set alternatives    model_function model_parameters   aic
#>    <chr>    <chr>        <chr>           <chr>          <list>           <dbl>
#>  1 1        ivs          iv1             lm             <prmtrs_m>        907.
#>  2 1        dvs          dv1             lm             <prmtrs_m>        907.
#>  3 1        include1     include1 == 0   lm             <prmtrs_m>        907.
#>  4 1        include2     include2 != 3   lm             <prmtrs_m>        907.
#>  5 1        include3     include3 > -2.5 lm             <prmtrs_m>        907.
#>  6 1        model        linear model    lm             <prmtrs_m>        907.
#>  7 2        ivs          iv1             lm             <prmtrs_m>        925.
#>  8 2        dvs          dv2             lm             <prmtrs_m>        925.
#>  9 2        include1     include1 == 0   lm             <prmtrs_m>        925.
#> 10 2        include2     include2 != 3   lm             <prmtrs_m>        925.
#> # ℹ 278 more rows
#> # ℹ 9 more variables: aicc <dbl>, bic <dbl>, r2 <dbl>, r2_adjusted <dbl>,
#> #   rmse <dbl>, sigma <dbl>, model_warnings <list>, model_messages <list>,
#> #   pipeline_code <list>

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 r² 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))
#> # A tibble: 1 × 3
#>   r2_mean r2_median r2_list   
#>     <dbl>     <dbl> <list>    
#> 1 0.00423   0.00304 <dbl [48]>

# model parameters for our predictor of interest
multiverse_results |>
  reveal_model_parameters() |> 
  filter(str_detect(parameter, "iv")) |>
  condense(unstd_coef, list(mean = mean, median = median))
#> # A tibble: 1 × 3
#>   unstd_coef_mean unstd_coef_median unstd_coef_list
#>             <dbl>             <dbl> <list>         
#> 1         0.00744            0.0121 <dbl [96]>

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(unstd_coef, list(mean = mean, median = median))
#> # A tibble: 6 × 5
#> # Groups:   ivs [3]
#>   ivs   dvs   unstd_coef_mean unstd_coef_median unstd_coef_list
#>   <chr> <chr>           <dbl>             <dbl> <list>         
#> 1 iv1   dv1           0.00259          0.000479 <dbl [16]>     
#> 2 iv1   dv2           0.0291           0.0313   <dbl [16]>     
#> 3 iv2   dv1          -0.0466          -0.0580   <dbl [16]>     
#> 4 iv2   dv2          -0.00744         -0.0143   <dbl [16]>     
#> 5 iv3   dv1           0.0524           0.0637   <dbl [16]>     
#> 6 iv3   dv2           0.0145           0.0182   <dbl [16]>

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(unstd_coef, list(mean = mean, median = median))
#> # A tibble: 16 × 5
#> # Groups:   parameter [8]
#>    parameter   dvs   unstd_coef_mean unstd_coef_median unstd_coef_list
#>    <chr>       <chr>           <dbl>             <dbl> <list>         
#>  1 (Intercept) dv1          -0.00608          -0.00498 <dbl [24]>     
#>  2 (Intercept) dv2          -0.0382           -0.0352  <dbl [24]>     
#>  3 iv1         dv1           0.0366            0.0383  <dbl [8]>      
#>  4 iv1         dv2           0.0192            0.0189  <dbl [8]>      
#>  5 iv1:mod     dv1          -0.0314           -0.0317  <dbl [8]>      
#>  6 iv1:mod     dv2           0.0390            0.0386  <dbl [8]>      
#>  7 iv2         dv1          -0.00203          -0.00349 <dbl [8]>      
#>  8 iv2         dv2           0.00431           0.00130 <dbl [8]>      
#>  9 iv2:mod     dv1          -0.0911           -0.0885  <dbl [8]>      
#> 10 iv2:mod     dv2          -0.0192           -0.0193  <dbl [8]>      
#> 11 iv3         dv1           0.0370            0.0416  <dbl [8]>      
#> 12 iv3         dv2           0.00137           0.00357 <dbl [8]>      
#> 13 iv3:mod     dv1           0.0679            0.0652  <dbl [8]>      
#> 14 iv3:mod     dv2           0.0277            0.0266  <dbl [8]>      
#> 15 mod         dv1           0.0110            0.0107  <dbl [24]>     
#> 16 mod         dv2           0.0202            0.0194  <dbl [24]>