Unpack a component of your analyzed grid

Usage

unpack_results(.multi, .what, .which = NULL, .unpack_specs = "wide")

unpack_model_parameters(.multi, effect_key = NULL, .unpack_specs = "wide")

unpack_model_performance(.multi, .unpack_specs = "wide")

unpack_model_warnings(.multi, .unpack_specs = "wide")

unpack_model_messges(.multi, .unpack_specs = "wide")

Arguments

.multi: a multiverse list-column tibble produced by analyze_grid.
.what: the name of a list-column you would like to unpack
.which: any sub-list columns you would like to unpack
.unpack_specs: character, options are "no", "wide", or "long". "no" (default) keeps specifications in a list column, wide unnests specifications with each specification category as a column. "long" unnests specifications and stacks them into long format, which stacks specifications into a decision_type, decision_set and decision_choice columns. This is mainly useful for plotting.
effect_key: character, if you added parameter keys to your pipeline, you can specify if you would like filter the parameters using one of your parameter keys. This is useful when different variables are being switched out across the multiverse but represent the same effect of interest.

Value

the unnested part of the multiverse requested. This usually contains the particular estimates or statistics you would like to analyze over the decision grid specified.

Functions

unpack_model_parameters(): Unpack the model parameters
unpack_model_performance(): Unpack the model performance
unpack_model_warnings(): Unpack the model warnings
unpack_model_messges(): Unpack the model messages

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_model("linear_model", lm({dvs} ~ {ivs} * {mods} + cov1))

pipeline_grid <- expand_decisions(full_pipeline)

# Run the whole multiverse
the_multiverse <- 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.

# Reveal results of the linear model
the_multiverse |> unpack_results(model_fitted, model_parameters)
#> Error: object 'the_multiverse' not found