add_postprocess {multitool}

Add arbitrary postprocessing code to a multiverse pipeline

Description

Add arbitrary postprocessing code to a multiverse pipeline

Usage

add_postprocess(.df, postprocess_name, code)

Arguments

.df

The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

postprocess_name

a character string. A descriptive name for what the postprocessing step accomplishes.

code

the literal code you would like to execute after each analysis.

The code should be written to work with pipes (i.e., |> or %>%). Because the post-processing code comes last in each multiverse analysis step, the chosen model object will be passed to the post-processing code.

For example, if you fit a simple linear model like: lm(y ~ x1 + x2), and your post-processing code executes a call to anova, you would simply pass anova() to add_postprocess(). The underlying code would be:

data |> filters |> lm(y ~ x1 + x2, data = _) |> anova()

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples

library(tidyverse)
library(multitool)

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)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess("scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_model("linear model", lm({dvs} ~ {ivs} * {mods})) |>
  add_postprocess("analysis of variance", aov())