Add parameter keys names for later use in summarizing model effects

Usage

add_parameter_keys(.df, parameter_group, parameter_name)

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.
parameter_group: character, a name for the parameter of interest
parameter_name: quoted or unquoted names of variables involved in a particular parameter of interest. Usually this is just a variable in your model (e.g., a main effect of your iv). However, it could also be an interaction term or some other term. You can use glue syntax to specify an effect that might use alternative versions of the same variable.

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)

# 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)
  )

the_data |>
  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})) |>
  add_parameter_keys("my_interaction", "{ivs}:{mods}") |>
  add_parameter_keys("my_main_effect", {ivs})
#> # A tibble: 11 × 4
#>    type          group          code                       additional_args
#>    <chr>         <chr>          <chr>                      <lgl>          
#>  1 variables     ivs            iv1                        NA             
#>  2 variables     ivs            iv2                        NA             
#>  3 variables     ivs            iv3                        NA             
#>  4 variables     dvs            dv1                        NA             
#>  5 variables     dvs            dv2                        NA             
#>  6 variables     mods           mod1                       NA             
#>  7 variables     mods           mod2                       NA             
#>  8 variables     mods           mod3                       NA             
#>  9 models        linear model   lm({dvs} ~ {ivs} * {mods}) NA             
#> 10 parameter_key my_interaction {ivs}:{mods}               NA             
#> 11 parameter_key my_main_effect {ivs}                      NA