Technical Appendix: Workflow of `do_boot()`

In manymome: Mediation, Moderation and Moderated-Mediation After Model Fitting

library(DiagrammeR)

Goal

This technical appendix describes how do_boot() from the package manymome (Cheung & Cheung, 2024) works to generate bootstrap estimates to be used by other functions to form confidence intervals.

do_boot()

mermaid("
flowchart TD
  classDef default fill:#EEEEFF;
  classDef errornode fill:#FFDDDD;
  classDef startend fill:#DDFFDD;
  classDef mcnode fill:#FFFFDD;
  classDef bootnode fill:#DDFFFF;
  classDef lavnode fill:#FFDDFF;
  classDef lmnode fill:#FFDDDD;
  classDef subnode fill:#FFFFDD;

  ZZ([\"Start\"])
  Z{{\"How was the model fitted?\"}}
  ZZ:::startend --> Z
%%  Zla[\"Fitted by lavaan()\"]
%%  Zlm[\"Fitted by lm()\"]
%%  Z -- lm --> Zlm
%%  Z -- lavaan --> Zla
  Z -- lavaan --> A
  Z -- lm --> B

  subgraph lavaan [ ]
  A{{\"Are bootstrap estimates stored?\"}}
  AYes[[\"Call fit2boot_out()\"]]
  ANo[[\"Call fit2boot_out_do_boot()\"]]
  end

%%  Zla --> A
  A -- Yes --> AYes:::subnode
  A -- No --> ANo:::subnode

  subgraph lm [ ]
  B{{\"Parallel processing?\"}}
  BYes[[\"Call lm2boot_out_parallel()\"]]
  BNo[[\"Call lm2boot_out()\"]]
  end

%%  Zlm --> B
  B -- Yes --> BYes:::subnode
  B -- No --> BNo:::subnode

  C([\"Return a boot_out-class object\"])
  AYes --> C:::startend
  ANo --> C
  BYes --> C
  BNo --> C
", height = 400, width = 800)

lavaan

fit2boot_out()

It retrieves stored bootstrap estimates.

mermaid("
flowchart TD
  classDef default fill:#EEEEFF;
  classDef errornode fill:#FFDDDD;
  classDef startend fill:#DDFFDD;
  classDef mcnode fill:#FFFFDD;
  classDef bootnode fill:#DDFFFF;
  classDef lavnode fill:#FFDDFF;
  classDef lmnode fill:#FFDDDD;
  classDef subnode fill:#FFFFDD;

  ZZ([\"Start\"])
  ZZ:::startend --> A

  A[\"boot_est <- boot2est()\"]
  B[\"boot_implied <- boot2implied()\"]
  B2[\"Combine boot_est and boot_implied\"]
  C([\"Return a boot_out-class object\"])
  A --> B
  B --> B2
  B2 --> C:::startend
", height = 400)

fit2boot_out_do_boot()

mermaid("
flowchart TD
  classDef default fill:#EEEEFF;
  classDef errornode fill:#FFDDDD;
  classDef startend fill:#DDFFDD;
  classDef mcnode fill:#FFFFDD;
  classDef bootnode fill:#DDFFFF;
  classDef lavnode fill:#FFDDFF;
  classDef lmnode fill:#FFDDDD;
  classDef subnode fill:#FFFFDD;

  ZZ([\"Start\"])
  ZZ:::startend --> A

  A[\"Call gen_boot_i() to create<br>a function, boot_i(), for doing one bootstrap replication\"]
  B[\"Generate indices (ids) locally\"]
  A --> B
  C[\"Call boot_i() R times\"]
  D[\"Keep only replications with valid values\"]
  E([\"Return a boot_out-class object\"])
  B --> C
  C --> D
  D --> E:::startend
", height = 500)

lm

lm2boot_out_parallel()

mermaid("
flowchart TD
  classDef default fill:#EEEEFF;
  classDef errornode fill:#FFDDDD;
  classDef startend fill:#DDFFDD;
  classDef mcnode fill:#FFFFDD;
  classDef bootnode fill:#DDFFFF;
  classDef lavnode fill:#FFDDFF;
  classDef lmnode fill:#FFDDDD;
  classDef subnode fill:#FFFFDD;

  ZZ([\"Start\"])
  ZZ:::startend --> A

  A[\"Call merge_model_frame() to reconstruct the data\"]
  B[\"Generate indices (ids) locally\"]
  A --> B
  C[\"Call lm_boot2est_i() R times\"]
  E([\"Return a boot_out-class object\"])
  B --> C
  C --> E:::startend
", height = 450)

lm2boot_out()

mermaid("
flowchart TD
  classDef default fill:#EEEEFF;
  classDef errornode fill:#FFDDDD;
  classDef startend fill:#DDFFDD;
  classDef mcnode fill:#FFFFDD;
  classDef bootnode fill:#DDFFFF;
  classDef lavnode fill:#FFDDFF;
  classDef lmnode fill:#FFDDDD;
  classDef subnode fill:#FFFFDD;

  ZZ([\"Start\"])
  ZZ:::startend --> A

  A[\"Call merge_model_frame() to reconstruct the data\"]
  C[\"Call lm_boot2est_i() R times\"]
  E([\"Return a boot_out-class object\"])
  A --> C
  C --> E:::startend
", height = 380)

Notes

Extracting Point Estimates

When the point estimates are needed, they will be extracted internally using functions developed for the fit object, which can be a lavaan-class object, a list of the outputs from stats::lm(), or a lavaan.mi-class object generated by fitting a model to several datasets using multiple imputation.

Reference

Cheung, S. F., & Cheung, S.-H. (2024). manymome: An R package for computing the indirect effects, conditional effects, and conditional indirect effects, standardized or unstandardized, and their bootstrap confidence intervals, in many (though not all) models. Behavior Research Methods, 56(5), 4862--4882. https://doi.org/10.3758/s13428-023-02224-z

manymome documentation built on June 21, 2025, 9:07 a.m.