Add parameter keys names for later use in summarizing model effects

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 × 6
#>    type          group    code  model_coefs_fn model_fit_fn model_standardize_fn
#>    <chr>         <chr>    <chr> <chr>          <chr>        <chr>               
#>  1 variables     ivs      iv1   NA             NA           NA                  
#>  2 variables     ivs      iv2   NA             NA           NA                  
#>  3 variables     ivs      iv3   NA             NA           NA                  
#>  4 variables     dvs      dv1   NA             NA           NA                  
#>  5 variables     dvs      dv2   NA             NA           NA                  
#>  6 variables     mods     mod1  NA             NA           NA                  
#>  7 variables     mods     mod2  NA             NA           NA                  
#>  8 variables     mods     mod3  NA             NA           NA                  
#>  9 models        linear … lm({… parameters::p… performance… parameters::standar…
#> 10 parameter_key my_inte… {ivs… NA             NA           NA                  
#> 11 parameter_key my_main… {ivs} NA             NA           NA