reveal_corrs {multitool}

Reveal a set of multiverse correlations

Description

Reveal a set of multiverse correlations

Usage

reveal_corrs(.descriptives, .which, .unpack_specs = "no")

Arguments

.descriptives

a descriptive multiverse list-column tibble produced by run_descriptives.

.which

the specific name of the correlations requested

.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_set and alternatives columns. This is mainly useful for plotting.

Value

an unnested set of correlations per decision from the multiverse.

Examples

library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = 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)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(
    include1 == 0,
    include2 != 3,
    include2 != 2,
    include3 > -2.5,
    include3 < 2.5,
    between(include3, -2.5, 2.5)
  ) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_correlations("predictors", starts_with("iv")) |>
  add_summary_stats("iv_stats", starts_with("iv"), c("mean", "sd")) |>
  add_reliabilities("vio_scale", starts_with("iv")) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

my_descriptives <- run_descriptives(full_pipeline)

my_descriptives |>
  reveal_corrs(predictors_rs)