# More examples of R6 classes

This vignette serves as a code reference for methods of the R6 classes in shinymrp. Unlike Programmatic workflow demonstration, the examples here are self-contained, focusing on individual methods rather than a complete workflow.

## MRPWorkflow

### compare_models()

Compare multiple fitted models

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create and fit multiple models
    model1 <- workflow$create_model(
      intercept_prior = "normal(0, 4)",
      fixed = list(
        sex = "normal(0, 2)"
      ),
      varying = list(
        race = "normal(0, 2)",
        age = "normal(0, 2)",
        time = "normal(0, 2)"
      )
    )

    model2 <- workflow$create_model(
      intercept_prior = "normal(0, 4)",
      fixed = list(
        sex = "normal(0, 2)"
      ),
      varying = list(
        race = "normal(0, 2)",
        age = "normal(0, 2)",
        time = "normal(0, 2)"
      ),
      interaction = list(
        `sex:time` = "normal(0, 2)",
        `race:time` = "normal(0, 2)",
        `age:time` = "normal(0, 2)"
      )
    )

    # Fit all models
    model1$fit(n_iter = 500, n_chains = 2, seed = 123)
    model2$fit(n_iter = 500, n_chains = 2, seed = 123)

    # Compare models
    comparison <- workflow$compare_models(model1, model2)

### covar_hist()

Create geographic covariate distribution histogram

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = "covid",
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create and (optionally) save a covariate distribution histogram
    # with dimensions in inches
    college_hist <- workflow$covar_hist(
      "college",
      file = "college_hist.png",
      width = 18,
      height = 8,
      units = "in"
    )

### create_model()

Create a new MRPModel object

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create a new model object
    model <- workflow$create_model(
      intercept_prior = "normal(0, 4)",
      fixed = list(
        sex = "normal(0, 2)"
      ),
      varying = list(
        race = "normal(0, 2)",
        age = "normal(0, 2)",
        time = "normal(0, 2)"
      ),
      interaction = list(
        `sex:time` = "normal(0, 2)",
        `race:time` = "normal(0, 2)",
        `age:time` = "normal(0, 2)"
      )
    )

### demo_bars()

Create demographic comparison bar plots

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create and (optionally) save a demographic comparison plot
    # with dimensions in inches
    sex_bar <- workflow$demo_bars(
      demo = "sex",
      file = "sex_bar.png",
      width = 18,
      height = 8,
      units = "in"
    )

### estimate_map()

Create a choropleth map of MRP estimates

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    example_model <- example_model()

    # Create and (optionally) save an interactive map of MRP estimates
    est_map <- workflow$estimate_map(
      example_model,
      geo = "county",
      file = "est_map.html"
    )

### estimate_plot()

Visualize estimates for demographic groups

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    example_model <- example_model()

    # Create and (optionally) save a plot of overall estimates
    # with dimensions in inches
    p_est_overall <- workflow$estimate_plot(
      example_model,
      width = 18,
      height = 8,
      units = "in"
    )

    # Create estimate plots for sex with 90 percent confidence intervals
    p_est_sex <- workflow$estimate_plot(
      example_model,
      group = "sex",
      interval = 0.9
    )

### link_acs()

Link sample data to ACS data

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

### load_pstrat()

Load custom poststratification data

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example sample data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Load example poststratification data
    pstrat_data <- example_pstrat_data()

    # Read in poststratification data
    workflow$load_pstrat(
      pstrat_data = pstrat_data,
      is_aggregated = TRUE
    )

### outcome_map()

Visualize raw outcome measure by geography

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create and (optionally) save an interactive map
    # of average outcome measure
    oc_map <- workflow$outcome_map(
      summary_type = "max",
      file = "outcome_map.html"
    )

### outcome_plot()

Create summary plots of the outcome measure

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create and (optionally) save outcome plots
    # with dimensions in inches
    oc_plot <- workflow$outcome_plot(
      file = "outcome_plot.png",
      width = 18,
      height = 8,
      units = "in"
    )

### pp_check()

Perform posterior predictive check

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    example_model <- example_model()

    # Perform posterior predictive check and (optionally) save the plot
    ppc_plot <- workflow$pp_check(example_model, file = "ppc_plot.png")

### preprocess()

Preprocess sample data

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

### preprocessed_data()

Return preprocessed sample data

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Get the preprocessed data
    preprocessed <- workflow$preprocessed_data()

### sample_size_map()

Create sample size map

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create and (optionally) save an interactive sample size map
    ss_map <- workflow$sample_size_map(file ="sample_size_map.html")

## MRPModel

### check_estimate_exists()

Check if poststratification has been performed

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create and fit multiple models
    model <- workflow$create_model(
      intercept_prior = "normal(0, 4)",
      fixed = list(
        sex = "normal(0, 2)"
      ),
      varying = list(
        race = "normal(0, 2)",
        age = "normal(0, 2)",
        time = "normal(0, 2)"
      )
    )

    # Fit the model
    model$fit(n_iter = 500, n_chains = 2, seed = 123)

    # Check if estimates exist (should be FALSE)
    model$check_estimate_exists()

    # Run poststratification
    # NOTE: $poststratify() is called by $estimate_plot() of a MRPWorkflow object
    workflow$estimate_plot(model)

    # Check again (should be TRUE)
    model$check_estimate_exists()

### check_fit_exists()

Check if model has been fitted

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create and fit multiple models
    model <- workflow$create_model(
      intercept_prior = "normal(0, 4)",
      fixed = list(
        sex = "normal(0, 2)"
      ),
      varying = list(
        race = "normal(0, 2)",
        age = "normal(0, 2)",
        time = "normal(0, 2)"
      )
    )

    # Check if model is fitted (should be FALSE)
    model$check_fit_exists()

    # Fit the model
    model$fit(n_iter = 500, n_chains = 2, seed = 123)

    # Check again (should be TRUE)
    model$check_fit_exists()

### diagnostics()

Return sampling diagnostics

    library(shinymrp)

    # Load example data
    example_model <- example_model()

    # Get diagnostics summary
    diagnostics_summary <- example_model$diagnostics(summarize = TRUE)

    # Get raw diagnostics of individual chains
    diagnostics_detailed <- example_model$diagnostics(summarize = FALSE)

### fit()

Fit multilevel regression model using cmdstanr

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example data
    sample_data <- example_sample_data()

    # Preprocess sample data
    workflow$preprocess(
      sample_data,
      is_timevar = TRUE,
      is_aggregated = TRUE,
      special_case = NULL,
      family = "binomial"
    )

    # Link to ACS data at ZIP code level
    workflow$link_acs(
      link_geo = "zip",
      acs_year = 2021
    )

    # Create and fit multiple models
    model <- workflow$create_model(
      intercept_prior = "normal(0, 4)",
      fixed = list(
        sex = "normal(0, 2)"
      ),
      varying = list(
        race = "normal(0, 2)",
        age = "normal(0, 2)",
        time = "normal(0, 2)"
      )
    )

    # Run MCMC
    model$fit(n_iter = 500, n_chains = 2, seed = 123)

### formula()

Return model formula

    library(shinymrp)

    # Load example data
    example_model <- example_model()

    # Get model formula
    formula <- example_model$formula()

### metadata()

Return model metadata

    library(shinymrp)

    # Load example data
    example_model <- example_model()

    # Get model metadata
    metadata <- example_model$metadata()

### model_spec()

Return model specification

    library(shinymrp)

    # Load example data
    example_model <- example_model()

    # Get model specification list
    spec <- example_model$model_spec()

### save()

Save model object to file

    library(shinymrp)

    # Initialize workflow
    workflow <- mrp_workflow()

    # Load example model
    example_model <- example_model()

    # Save model to file
    example_model$save("model.qs2")

    # Load model later
    loaded_model <- qs2::qs_read("model.qs2")

### stan_code()

Return model Stan code.

    library(shinymrp)

    # Load example data
    example_model <- example_model()

    # Get Stan code
    stan_code <- example_model$stan_code()

### summary()

Return posterior summary table

    library(shinymrp)

    # Load example data
    example_model <- example_model()

    # Get parameter summaries
    parameter_summary <- example_model$summary()

    # Fixed effects
    parameter_summary$fixed

    # Standard deviation of varying effects
    parameter_summary$varying

    # Standard deviation of residuals
    parameter_summary$other