reveal_model_performance {multitool}

Reveal the model performance/fit indices from a multiverse analysis

Description

Reveal the model performance/fit indices from a multiverse analysis

Usage

reveal_model_performance(.multi, .unpack_specs = "no")

Arguments

.multi

a multiverse list-column tibble produced by run_multiverse.

.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

the unnested model performance/fit indices from a multiverse analysis.

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 <- run_multiverse(pipeline_grid[1:10,])

# Reveal results of the linear model
the_multiverse |>
  reveal_model_performance()