Technical Appendix: Workflow of `do_mc()`
In manymome: Mediation, Moderation and Moderated-Mediation After Model Fitting

library(DiagrammeR)

Goal

This technical appendix describes how do_mc() from the package manymome (Cheung & Cheung, 2023) works to generate Monte Carlo estimates to be used by other functions to form confidence intervals.

`do_mc()`

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 --> Z

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

  subgraph lm [ ]
  B([\"Raise an error. lm() not supported.\"])
  end

  subgraph lavaan [ ]
  A[\"Call gen_mc_est() to generate Monte Carlo estimates\"]
  A2[[\"Call fit2mc_out() to generate the implied statistics\"]]
  end

  A --> A2:::subnode
  C([\"Return a mc_out-class object\"])
  A2 --> C:::startend
", height = 480, width = 800)

`fit2mc_out()`

It retrieves stored Monte Carlo 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[\"mc_est <- mc2est()\"]
  B[\"mc_implied <- mc2implied()\"]
  B2[\"Combine mc_est and mc_implied\"]
  C([\"Return a mc_out-class object\"])
  A --> B
  B --> B2
  B2 --> C:::startend
", height = 380)

Notes

Extracting Point Estimates and Variance-Covariance Matrix

When the point estimates or variance-covariance matrix of 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.