MRPModel objects — MRPModel • shinymrp

An MRPModel object is an R6 object created by the $create_model() method of an MRPWorkflow object. Each MRPModel object represents a multilevel regression model, providing methods for sampling, diagnostics, and poststratification.

Creates a new instance of the MRPModel class. This method is called by the $create_model() method of an MRPWorkflow object and does not need to be called directly by users.

Arguments

model_spec: List containing model effects specification, including intercept, fixed effects, varying effects, and interactions
mrp_data: List containing the MRP data structure with input sample data and new poststratification data
metadata: List containing metadata about the analysis, including family, time variables, and special cases
link_data: List containing information about data linking, including geography and ACS year
plot_data: List containing data prepared for visualization, including dates and GeoJSON objects
extra: List containing COVID test sensitivity and specificity

Value

A new MRPModel object initialized with the provided model specification and relevant data.

Methods

MRPModel objects have the following associated methods, many of which have their own (linked) documentation pages:

Data access

Method	Description
`$model_spec()`	Return model specification.
`$formula()`	Return model formula.
`$metadata()`	Return model metadata.
`$stan_code()`	Return model Stan code.

Model fitting

Method	Description
`$fit()`	Fit multilevel regression model using CmdStanR.
`$check_fit_exists()`	Check if model has been fitted.
`$check_estimate_exists()`	Check if poststratification has been performed.

Posterior summary & diagnostics

Method	Description
`$summary()`	Return posterior summary table.
`$diagnostics()`	Return sampling diagnostics.

Post-processing

Method	Description
`$ppc()`	Create input for posterior predictive check.
`$log_lik()`	Create input for leave-one-out cross-validation.
`$poststratify()`	Run poststratification to generate population estimates.

Saving model object

Method	Description
`$save()`	Save model object to file.

Examples

 # \donttest{
   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"
   )
#> Preprocessing sample data...

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

   # 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)
#> Running MCMC with 2 parallel chains, with 1 thread(s) per chain...
#> 
#> Chain 1 Iteration:   1 / 500 [  0%]  (Warmup) 
#> Chain 2 Iteration:   1 / 500 [  0%]  (Warmup) 
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#> Chain 2 Iteration: 400 / 500 [ 80%]  (Sampling) 
#> Chain 1 Iteration: 500 / 500 [100%]  (Sampling) 
#> Chain 1 finished in 1.8 seconds.
#> Chain 2 Iteration: 450 / 500 [ 90%]  (Sampling) 
#> Chain 2 Iteration: 500 / 500 [100%]  (Sampling) 
#> Chain 2 finished in 2.0 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 1.9 seconds.
#> Total execution time: 2.2 seconds.
#> 
#> Warning: 4 of 500 (1.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.

   # Estimates summary and diagnostics
   posterior_summary <- model$summary()

   # Sampling diagnostics
   model_diagnostics <- model$diagnostics()
 # }