Perform all analyses over a complete decision grid

Usage

analyze_grid(
  .grid,
  save_model = FALSE,
  show_progress = TRUE,
  libraries = NULL,
  ...
)

Arguments

.grid: a tibble produced by expand_decisions
save_model: logical, indicates whether to save the model object in its entirety. The default is FALSE because model objects are usually large and under the hood, parameters and performance is used to summarize the most useful model information.
show_progress: logical, whether to show a progress bar while running.
libraries: a vector of character strings naming the packages you want to load when executing parallel processing. Internally, this will call library dynamically to ensure that any functions specific to a package you are using are available during execution on the individual workers. Only relevant if you have called mirai::daemons().
...: this also reserved for parallel processing. Any custom functions you might use your pipeline (e.g., a custom post processin step), can be passed here in the form of custom_func = custom_func. This will be passed along to purrr::in_parallel to make them available on the independent workers.

Value

a single tibble containing tidied results for the model and any post-processing tests/tasks. For each unique test (e.g., an lm or aov called on an lm), a list column with the function name is created with parameters and performance and any warnings or messages printed while fitting the models. Internally, modeling and post-processing functions are checked to see if there are tidy or glance methods available. If not, summary will be called instead.

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)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess(process_name = "scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_preprocess(process_name = "scale_mod", mutate({mods} := scale({mods}))) |>
  add_model("no covariates",lm({dvs} ~ {ivs} * {mods})) |>
  add_model("covariate", lm({dvs} ~ {ivs} * {mods} + cov1)) |>
  add_postprocess("aov", aov())

pipeline_grid <- expand_decisions(full_pipeline)

# analyze the grid
analyzed_grid <- analyze_grid(pipeline_grid[1:10,])
#> Error in parallel_pkgs_installed(): The packages "carrier" (>= 0.3.0) and "mirai" (>= 2.5.1) are required
#> for parallel map.