Add a model and formula to a multiverse pipeline

Add a model and formula to a multiverse pipeline

Usage

add_model(
  .df,
  model_desc,
  code,
  model_coefs = parameters::parameters(),
  model_fit = performance::performance(),
  model_standardize = parameters::standardize_parameters()
)

Arguments

.df

The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

model_desc

a human readable name you would like to give the model.

code

literal model syntax you would like to run. You can use glue inside formulas to dynamically generate variable names based on a variable grid. For example, if you make variable grid with two versions of your IVs (e.g., iv1 and iv2), you can write your formula like so: lm(happiness ~ {iv} + control_var). The only requirement is that the variables written in the formula actually exist in the underlying data. You are also responsible for loading any packages that run a particular model (e.g., lme4 for mixed-models)

model_coefs

a function to extract coefficients from the model object. The default is to use parameters::parameters() but this could be also be broom::tidy() or any other function that summarizes model output. Whichever function you choose must take a model object as the first argument and return a data.frame.

model_fit

a function to summarize model fit statistics. The default is to use performance::performance() but this could be also be broom::glance() or any other function that summarizes model output. Whichever function you choose must take a model object as the first argument and return a data.frame.

model_standardize

a function to calculate standardized coefficients from the model object. The default is to use parameters::standardize_parameters() but this could be also be some other function that standardizes model output. Whichever function you choose must take a model object as the first argument and return a data.frame.

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples

library(tidyverse)
library(multitool)

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)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess("scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_model("linear model", lm({dvs} ~ {ivs} * {mods}))
#> # A tibble: 17 × 6
#>    type       group       code  model_coefs_fn model_fit_fn model_standardize_fn
#>    <chr>      <chr>       <chr> <chr>          <chr>        <chr>               
#>  1 filters    include1    incl… NA             NA           NA                  
#>  2 filters    include1    incl… NA             NA           NA                  
#>  3 filters    include2    incl… NA             NA           NA                  
#>  4 filters    include2    incl… NA             NA           NA                  
#>  5 filters    include2    incl… NA             NA           NA                  
#>  6 filters    include3    incl… NA             NA           NA                  
#>  7 filters    include3    incl… NA             NA           NA                  
#>  8 variables  ivs         iv1   NA             NA           NA                  
#>  9 variables  ivs         iv2   NA             NA           NA                  
#> 10 variables  ivs         iv3   NA             NA           NA                  
#> 11 variables  dvs         dv1   NA             NA           NA                  
#> 12 variables  dvs         dv2   NA             NA           NA                  
#> 13 variables  mods        mod1  NA             NA           NA                  
#> 14 variables  mods        mod2  NA             NA           NA                  
#> 15 variables  mods        mod3  NA             NA           NA                  
#> 16 preprocess scale_iv    muta… NA             NA           NA                  
#> 17 models     linear mod… lm({… parameters::p… performance… parameters::standar…