View source: R/unpack-multiverse.R
reveal_model_parameters | R Documentation |
Reveal the model parameters of a multiverse analysis
reveal_model_parameters(.multi, parameter_key = NULL, .unpack_specs = "no")
.multi |
a multiverse list-column |
parameter_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. |
.unpack_specs |
character, options are |
the unnested model paramerters from the multiverse.
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_parameters()
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