Rmissax Extra Quality Full

I think there may be a misunderstanding here.

"Rmissax" doesn't seem to be a known term or a feature in any popular software or technology. Could you please provide more context or information about where you encountered this term?

If you're looking for information on a specific feature or tool, I'd be happy to help you with that. Just let me know what you're trying to accomplish or what you're interested in learning more about! rmissax full

If you are new to the package, start with the quick‑start section; if you already have it installed and want to dive straight into the full‑mode workflow, skip ahead to the “Full‑Mode Feature Walk‑through”.

3.4. Multiple Imputation Engine (MICE‑style)

The heavy‑lifting step. By default it creates 5 multiply‑imputed datasets, but you can change n_imp. I think there may be a misunderstanding here

imp_res <- impute_multiple(df = my_data,
                           method_tbl = method_tbl,
                           n_imp = 5,
                           seed = 2026,
                           parallel = TRUE)   # uses `future.apply` for speed

Key goodies

• Automatic predictor matrix – drops collinear predictors and respects the missingness‑mechanism results.
• Convergence diagnostics – trace plots for each variable are stored in imp_res$diagnostics$trace.
• Pooling – pool_estimates() will combine regression coefficients across imputations (Rubin’s rules).

4.2 Primary Subcommands

2️⃣ Quick‑Start (Installation & a one‑liner)

# Install from CRAN (or use devtools::install_github if you need the bleeding‑edge version)
install.packages("RmissAX")
# Load the library
library(RmissAX)
# Run the *full* pipeline on any data frame (e.g., the built‑in airquality data)
completed_df <- run_full(airquality, 
                         impute_method = "auto",   # automatically pick best method per variable
                         n_imp = 5,                # generate 5 multiply‑imputed datasets
                         seed = 2026)              # reproducibility

Result: completed_df is a list with

| Element | Description | |--------|-------------| | imputed_data | The final pooled dataset (or a list of n_imp imputed tables). | | diagnostics | A tibble summarising missingness patterns, MCAR/MAR tests, and convergence stats. | | plots | A list of ggplot objects (heatmaps, missingness maps, density comparisons). | | report | An auto‑generated HTML report (saved to the working directory). |