Package 'shinymrp' reference manual

Title:	Interface for Multilevel Regression and Poststratification
Description:	Dual interfaces, graphical and programmatic, designed for intuitive applications of Multilevel Regression and Poststratification (MRP). Users can apply the method to a variety of datasets, from electronic health records to sample survey data, through an end-to-end Bayesian data analysis workflow. The package provides robust tools for data cleaning, exploratory analysis, flexible model building, and insightful result visualization. For more details, see Si et al. (2020) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2020002/article/00003-eng.pdf?st=iF1_Fbrh> and Si (2025) <doi:10.1214/24-STS932>.
Authors:	Toan Tran [cre, aut, cph], Jonah Gabry [aut, cph], Yajuan Si [aut, cph]
Maintainer:	Toan Tran <[email protected]>
License:	MIT + file LICENSE
Version:	0.10.0
Built:	2026-06-02 09:39:37 UTC
Source:	https://github.com/mrp-interface/shinymrp

Return example `MRPModel` object with estimation results.

Description

Return an example MRPModel object with estimation results.

Usage

example_model(is_timevar = TRUE)
example_model(is_timevar = TRUE)

Arguments

is_timevar

Logical indicating whether the model is fitted to time-varying data.

Value

A MRPModel object.

Return example poststratification data

Description

Return example poststratification data accepted by the ⁠$load_pstrat()⁠ method of an MRPWorkflow object.

Usage

example_pstrat_data()
example_pstrat_data()

Value

A data.frame object.

Return example data

Description

Return example data based on the specified characteristics.

Usage

example_sample_data(
  is_timevar = TRUE,
  is_aggregated = TRUE,
  special_case = NULL,
  family = "binomial"
)
example_sample_data(
  is_timevar = TRUE,
  is_aggregated = TRUE,
  special_case = NULL,
  family = "binomial"
)

Arguments

is_timevar

Logical indicating whether the data is time-varying.

is_aggregated

Logical indicating whether the data is aggregated.

special_case

Optional character string for specific use cases such as COVID data. Options are NULL, "covid" and "poll". The default is NULL which indicates the data is not specific to any supported use case.

family

Character string specifying the distribution family for outcome measures. Options are "binomial" for binary outcomes and "normal" for continuous outcomes.

Value

A data.frame object.

Create a new MRPWorkflow object

Description

Create a new MRPWorkflow object that implements the Bayesian data analysis workflow common in applications of Multilevel Regression and Post-stratification (MRP).

Usage

mrp_workflow()
mrp_workflow()

Value

A MRPWorkflow object.

MRPModel objects

Description

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


 
   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)

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

   # Sampling diagnostics
   model_diagnostics <- model$diagnostics()
 

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)

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

   # Sampling diagnostics
   model_diagnostics <- model$diagnostics()

Check if poststratification has been performed

Description

The ⁠$check_estimate_exists()⁠ method of an MRPModel object checks whether poststratification has been performed. Check out the More examples of R6 classes vignette for usage examples.

Usage

check_estimate_exists()
check_estimate_exists()

Value

Logical indicating whether poststratification has been performed.

Check if model has been fitted

Description

The ⁠$check_fit_exists()⁠ method of an MRPModel object checks whether the model has been fitted. Check out the More examples of R6 classes vignette for usage examples.

Usage

check_fit_exists()
check_fit_exists()

Value

Logical indicating whether the model has been fitted.

Return sampling diagnostics

Description

The ⁠$diagnostics()⁠ method of an MRPModel object returns MCMC diagnostics, including convergence statistics and sampling efficiency measures. Check out this official Stan guide for more information on interpreting these metrics. For usage examples, refer to the More examples of R6 classes vignette.

Usage

diagnostics(summarize = TRUE)
diagnostics(summarize = TRUE)

Arguments

summarize

Logical indicating whether to return a summarized version of the diagnostics (default is TRUE)

Value

A data.frame object if summarize is TRUE, otherwise a list of raw diagnostics.

Fit multilevel regression model using cmdstanr

Description

The ⁠$fit()⁠ method of an MRPModel object fits the model using Stan's main Markov chain Monte Carlo (MCMC) algorithm. Check out the More examples of R6 classes vignette for usage examples.

Usage

fit(n_iter = 2000, n_chains = 4, seed = NULL, ...)
fit(n_iter = 2000, n_chains = 4, seed = NULL, ...)

Arguments

n_iter

Number of MCMC iterations per chain (including warmup iterations). Default is 2000.

n_chains

Number of MCMC chains to run. Default is 4.

seed

Random seed for reproducibility. Default is NULL.

...

poststratify(interval = 0.95)
poststratify(interval = 0.95)

Arguments

interval

Confidence interval (a numeric value between 0 and 1) or standard deviation ("1sd" or "2sd") for the estimates (default is 0.95).

Value

A data.frame object containing the poststratified estimates and their corresponding uncertainty intervals.

Create input for posterior predictive check

Description

The ⁠$ppc()⁠ method of an MRPModel object runs Stan's standalone generated quantities to draw from the posterior predictive distribution. This method is called by the ⁠$pp_check()⁠ method of a MRPWorkflow object and does not need to be called directly by users.

Usage

ppc()
ppc()

Value

A data.frame object containing samples from the posterior predictive distribution.

Save model object to file

Description

The ⁠$save()⁠ method of an MRPModel object saves a fitted MRPModel object to a file for later use. qs2::qs_save() is used internally, and it is customary to use the .qs2 file extension. Check out the More examples of R6 classes vignette for usage examples.

Usage

save(file)
save(file)

Arguments

file

File path where the model should be saved.

Value

No return value, called for side effects.

Return model Stan code.

Description

The ⁠$stan_code()⁠ method of an MRPModel object returns the model Stan code. Check out the More examples of R6 classes vignette for usage examples.

Usage

stan_code()
stan_code()

Value

A character string containing the model Stan code.

Return posterior summary table

Description

The ⁠$summary()⁠ method of an MRPModel object returns tables containing the summary of posterior samples for the model parameters and diagnostics. Check out the More examples of R6 classes vignette for usage examples.

Usage

summary()
summary()

Value

A list of data.frame objects containing posterior sample summary and diagnostics for model parameters:

fixed effects (fixed)
standard deviations of varying effects (varying)
standard deviations of residuals (residual)
BYM2 proportion of spatial structure (spatial_proportion)

MRPWorkflow objects

Description

A MRPWorkflow object is an R6 object created by the mrp_workflow() function. This class provides methods for all steps in the workflow, from data preparation and visualization to model fitting.

Methods

MRPWorkflow objects have the following associated methods with their own (linked) documentation pages:

Data preparation

Method	Description
`$preprocess()`	Preprocess sample data.
`$preprocessed_data()`	Return preprocessed sample data.
`$link_acs()`	Link sample data to ACS data.
`$load_pstrat()`	Load custom poststratification data.

Model fitting & diagnostics

Method	Description
`$create_model()`	Create a `MRPModel` object.
`$pp_check()`	Perform posterior predictive check.
`$compare_models()`	Compare models using LOO-CV.

Visualization

Method	Description
`$demo_bars()`	Create demographic comparison bar plots.
`$covar_hist()`	Create geographic covariate distribution histograms.
`$sample_size_map()`	Create sample size map.
`$outcome_plot()`	Create summary plots of raw outcome measure.
`$outcome_map()`	Visualize raw outcome measure by geography.
`$estimate_plot()`	Visualize estimates for demographic groups.
`$estimate_map()`	Visualize estimates for geographic areas.

Examples


 
   library(shinymrp)

   # Initialize the MRP workflow
   workflow <- mrp_workflow()

   # Load example data
   sample_data <- example_sample_data()

   ### DATA PREPARATION

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

   # Link data to the ACS
   # and obtain poststratification data
   workflow$link_acs(
     link_geo = "zip",
     acs_year = 2021
   )

   ### DESCRIPTIVE STATISTICS

   # Visualize demographic distribution of data
   sex_bars <- workflow$demo_bars(demo = "sex")

   # Visualize geographic distribution of data
   ss_map <- workflow$sample_size_map()

   # Visualize outcome measure
   raw_outcome_plot <- workflow$outcome_plot()

   ### MODEL BUILDING

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

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

   # Estimates summary and diagnostics
   model$summary()

   # Sampling diagnostics
   model$diagnostics()

   # Posterior predictive check
   workflow$pp_check(model)

   ### VISUALIZE RESULTS

   # Plots of overall estimates, estimates for demographic groups, and geographic areas
   workflow$estimate_plot(model, group = "sex")

   # Choropleth map of estimates for geographic areas
   workflow$estimate_map(model, geo = "county")
 

library(shinymrp)

   # Initialize the MRP workflow
   workflow <- mrp_workflow()

   # Load example data
   sample_data <- example_sample_data()

   ### DATA PREPARATION

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

   # Link data to the ACS
   # and obtain poststratification data
   workflow$link_acs(
     link_geo = "zip",
     acs_year = 2021
   )

   ### DESCRIPTIVE STATISTICS

   # Visualize demographic distribution of data
   sex_bars <- workflow$demo_bars(demo = "sex")

   # Visualize geographic distribution of data
   ss_map <- workflow$sample_size_map()

   # Visualize outcome measure
   raw_outcome_plot <- workflow$outcome_plot()

   ### MODEL BUILDING

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

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

   # Estimates summary and diagnostics
   model$summary()

   # Sampling diagnostics
   model$diagnostics()

   # Posterior predictive check
   workflow$pp_check(model)

   ### VISUALIZE RESULTS

   # Plots of overall estimates, estimates for demographic groups, and geographic areas
   workflow$estimate_plot(model, group = "sex")

   # Choropleth map of estimates for geographic areas
   workflow$estimate_map(model, geo = "county")

Compare models using LOO-CV

Description

The ⁠$compare_models()⁠ method compares multiple fitted MRPModel objects using leave-one-out cross-validation to assess relative model performance. Check out the More examples of R6 classes vignette for usage examples.

Usage

compare_models(..., suppress = NULL)
compare_models(..., suppress = NULL)

Arguments

...

Multiple MRPModel objects to compare.

suppress

Character string specifying output to suppress during comparison.

Value

A data.frame object containing the comparison results.

Create geographic covariate distribution histogram

Description

The covar_hist() method creates histogram plots showing the distribution of geographic covariates across ZIP codes. Refer to the More on data preparation for their definitions. This method is only available for COVID data. Check out the More examples of R6 classes vignette for usage examples.

Usage

covar_hist(covar, file = NULL, ...)
covar_hist(covar, file = NULL, ...)

Arguments

covar

Character string specifying the geographic covariate. Options are "college", "poverty", "employment", "income", "urbanicity", and "adi".

file

Optional file path to save the plot.

...

Additional arguments passed to ggsave, such as width and height.

Value

A ggplot object showing the covariate distribution histogram.

Create a new MRPModel object

Description

The ⁠$create_model()⁠ method creates a new MRPModel object with Stan code generated from the model specification list. CmdStanR objects are used internally to interface with Stan to compile the code and run its MCMC algorithm. Check out the More examples of R6 classes vignette for usage examples.

Usage

create_model(
  intercept_prior = NULL,
  fixed = NULL,
  varying = NULL,
  interaction = NULL,
  sens = 1,
  spec = 1
)
create_model(
  intercept_prior = NULL,
  fixed = NULL,
  varying = NULL,
  interaction = NULL,
  sens = 1,
  spec = 1
)

Arguments

intercept_prior

Character string specifying the prior distribution for the overall intercept. Check Details for more information about prior specification.

fixed

List of the fixed effects in the model and their prior distributions. Check Details for more information about prior specification.

varying

List of the varying effects in the model and the prior distributions of their standard deviations. Check Details for more information about prior specification.

interaction

List of the interactions in the model and their prior distributions. Interaction names are created by concatenating the names of the interacting variables with a colon (e.g., "sex:age"). Currently, only two-way interactions are supported. Check Details for more information about prior specification.

sens

Sensitivity adjustment in the COVID-19 test results. Check Details for more information.

spec

Specificity adjustment in the COVID-19 test results. Check Details for more information.

Details

Prior specification

The syntax for the prior distributions is similar to that of Stan. The following are currently supported:

normal(mu, sigma)
student_t(nu, mu, sigma)
structured
icar
bym2

The structured prior distribution developed by Si et al. (2020), which can be assigned to three types of two-way interactions:

Two categorical variables (both with more than two levels)
One categorical variable (with more than two levels) and one binary variable
One categorical variable (with more than two levels) and one continuous variable

The spatial prior options (ICAR and BYM2) are useful when data contain geographic units (e.g., ZIP codes, counties, states) with spatial structure, specifically when observations exhibit correlation among neighboring regions. For details about the implementation and usage, see Spatial prior specification in shinymrp.

The following default prior distributions are assigned to effects with empty strings ("") in the model specification list:

Overall intercept: normal(0,5)
Coefficient: normal(0,3)

The model assumes varying effects follow a normal distribution with an unknown standard deviation, which will be assigned with priors.

Standard deviation (main effect): normal₊(0,3)
Standard deviation (interaction): normal₊(0,1)

Testing sensitivity and specificity

For COVID data, we allow users to specify the PCR testing sensitivity and specificity. Let $p_k$ be the probability that person $i$ in group $k$ tests positive. The analytic incidence $p_k$ is a function of the test sensitivity $\delta$ , specificity $\gamma$ , and the true incidence $\pi_k$ for individuals in group $k$ :

$p_k=(1-\gamma)(1-\pi_k )+\delta \pi_k.$

Value

A new MRPModel object.

Create demographic comparison bar plots

Description

Creates bar plots for comparing demographic distributions between sample data and poststratification data. Check out the More examples of R6 classes vignette for usage examples.

Usage

demo_bars(demo, file = NULL, ...)
demo_bars(demo, file = NULL, ...)

Arguments

demo

Character string specifying the demographic variable to plot.

file

Optional file path to save the plot.

...

Additional arguments passed to ggsave, such as width and height.

Value

A ggplot object showing demographic comparisons

Create a choropleth map of MRP estimates

Description

The ⁠$estimate_map()⁠ method creates interactive choropleth maps that show MRP estimates by geographic region. This method cannot be used if either the sample or the poststratification data contains no geographic information. Check out the More examples of R6 classes vignette for usage examples.

Usage

estimate_map(
  model,
  geo = NULL,
  time_index = NULL,
  interval = 0.95,
  file = NULL
)
estimate_map(
  model,
  geo = NULL,
  time_index = NULL,
  interval = 0.95,
  file = NULL
)

Arguments

model

Fitted MRPModel object

geo

Character string specifying the geographic level for mapping. Options include geography for data linking and those at larger scales. A "linking" geography is required to use this method. It is either specified as geo in the ⁠$link_acs()⁠ method or the smallest common geographic scale between the sample data and the custom poststratification data input using ⁠$load_pstrat()⁠.

time_index

Integer specifying the time index for time-varying data.

interval

Confidence interval (a numeric value between 0 and 1) or standard deviation ("1sd" or "2sd") for the estimates (default is 0.95).

file

Optional file path with .html extension to save the interactive map. Expand the hamburger menu in the top right corner of the map to access other export options.

Value

A highcharter map object showing MRP estimates by geography.

Visualize estimates for demographic groups

Description

The ⁠$estimate_plot()⁠ method creates plots showing overall MRP estimates or estimates for different demographic groups. Check out the More examples of R6 classes vignette for usage examples.

Usage

estimate_plot(
  model,
  group = NULL,
  interval = 0.95,
  show_caption = TRUE,
  file = NULL,
  ...
)
estimate_plot(
  model,
  group = NULL,
  interval = 0.95,
  show_caption = TRUE,
  file = NULL,
  ...
)

Arguments

model

Fitted MRPModel object

group

Character string specifying the demographic group. If left as NULL, overall estimates are plotted.

interval

Confidence interval (a numeric value between 0 and 1) or standard deviation ("1sd" or "2sd") for the estimates (default is 0.95).

show_caption

Logical indicating whether to show the caption in the plot (default is TRUE).

file

Optional file path to save the plot.

...

Additional arguments passed to ggsave, such as width and height.

Value

A ggplot object showing MRP estimates.

Link sample data to ACS data

Description

The ⁠$link_acs()⁠ method obtains poststratification data by linking the preprocessed sample data to the American Community Survey based on given geographic granularity and year. See the More on data preparation vignette for more information on data linking. For usage examples, refer to the More examples of R6 classes vignette.

Usage

link_acs(link_geo = NULL, acs_year = 2023)
link_acs(link_geo = NULL, acs_year = 2023)

Arguments

link_geo

Character string specifying the geographic level for linking. Options are "zip", "county", and "state".

acs_year

Numeric value specifying the last year of the data collection period for the target ACS dataset.

Value

No return value, called for side effects.

Load custom poststratification data

Description

The ⁠$load_pstrat()⁠ method processes and stores input poststratification data. The object is subject to the same data preprocessing steps as the sample data. See the More on data preparation vignette for more information on data processing. For usage examples, refer to the More examples of R6 classes vignette.

Usage

load_pstrat(pstrat_data, is_aggregated = TRUE)
load_pstrat(pstrat_data, is_aggregated = TRUE)

Arguments

pstrat_data

An object of class data.frame (or one that can be coerced to that class).

is_aggregated

Logical indicating whether the poststratification data is already aggregated.

Value

No return value, called for side effects.

Visualize raw outcome measure by geography

Description

The ⁠$outcome_map()⁠ method creates maps showing the average outcome values by geography for cross-sectional data, or the highest/lowest temporal average for time-varying data. The sample and poststratification data must contain geographic information for this method to work. Check out the More examples of R6 classes vignette for usage examples.

Usage

outcome_map(summary_type = NULL, file = NULL)
outcome_map(summary_type = NULL, file = NULL)

Arguments

summary_type

Character string, for time-varying data, indicating whether to display the highest ("max") or lowest ("min") temporal average. Leave as NULL for cross-sectional data.

file

Optional file path with .html extension to save the interactive map. Expand the hamburger menu in the top right corner of the map to access other export options.

Value

A highcharter map object showing average outcome measure by geography.

Create summary plots of the outcome measure

Description

The ⁠$outcome_plot()⁠ method creates plots of the average outcome values. Check out the More examples of R6 classes vignette for usage examples.

Usage

outcome_plot(file = NULL, ...)
outcome_plot(file = NULL, ...)

Arguments

file

Optional file path to save the plot.

...

Additional arguments passed to ggsave, such as width and height.

Value

A ggplot object showing the outcome measure distribution.

Perform posterior predictive check

Description

The ⁠$pp_check()⁠ method creates posterior predictive check plots to assess model fit by comparing observed data to replicated data from the posterior predictive distribution. Check out the More examples of R6 classes vignette for usage examples.

Usage

pp_check(model, file = NULL, ...)
pp_check(model, file = NULL, ...)

Arguments

model

Fitted MRPModel object.

file

Optional file path to save the plot.

...

Additional arguments passed to ggsave, such as width and height.

Value

A ggplot object showing the posterior predictive check result.

Preprocess sample data

Description

The ⁠$preprocess()⁠ method runs the preprocessing pipeline that includes data standardization, filtering, imputation, and aggregation. See the More on data preparation vignette for more information about data processing. For usage examples, refer to the More examples of R6 classes vignette.

Usage

preprocess(
  data,
  is_timevar = FALSE,
  is_aggregated = FALSE,
  special_case = NULL,
  family = NULL,
  time_freq = NULL,
  freq_threshold = 0
)
preprocess(
  data,
  is_timevar = FALSE,
  is_aggregated = FALSE,
  special_case = NULL,
  family = NULL,
  time_freq = NULL,
  freq_threshold = 0
)

Arguments

data

An object of class data.frame (or one that can be coerced to that class) that satisfies the requirements specified in the More on data preparation vignette.

is_timevar

Logical indicating whether the data contains time-varying components.

is_aggregated

Logical indicating whether the data is already aggregated.

special_case

Character string specifying special case handling. Options are NULL (the default), "covid", and "poll".

family

Character string specifying the distribution family for the outcome variable. Options are "binomial" for binary outcome measures and "normal" for continuous outcome measures.

time_freq

Character string specifying the time indexing frequency or time length for grouping dates (YYYY-MM-DD) in the data. Options are NULL (the default), "week", "month", and "year". This parameter must be NULL for cross-sectional data or time-varying data that already has time indices.

freq_threshold

Numeric value specifying the minimum frequency threshold for including observations. Values with lower frequency will cause the entire row to be removed. The default value is 0 (no filtering).

Value

No return value, called for side effects.

Return preprocessed sample data

Description

The ⁠$preprocessed_data()⁠ method returns the preprocessed sample data. Check out the More examples of R6 classes vignette for usage examples.

Usage

preprocessed_data()
preprocessed_data()

Value

A data.frame object containing the preprocessed sample data.

Create sample size map

Description

The ⁠$sample_size_map()⁠ method creates interactive choropleth maps showing data distribution with respect to geography. This method cannot be used if either the sample or the poststratification data contains no geographic information. Check out the More examples of R6 classes vignette for usage examples.

Usage

sample_size_map(file = NULL)
sample_size_map(file = NULL)

Arguments

file

Optional file path with .html extension to save the interactive map. Expand the hamburger menu in the top right corner of the map to access other export options.

Value

A highcharter map object showing sample size distribution.

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(
  onStart = NULL,
  options = list(),
  enableBookmarking = NULL,
  uiPattern = "/",
  launch.browser = TRUE,
  ...
)
run_app(
  onStart = NULL,
  options = list(),
  enableBookmarking = NULL,
  uiPattern = "/",
  launch.browser = TRUE,
  ...
)

Arguments

onStart

A function that will be called before the app is actually run. This is only needed for shinyAppObj, since in the shinyAppDir case, a global.R file can be used for this purpose.

options

Named options that should be passed to the runApp call (these can be any of the following: "port", "launch.browser", "host", "quiet", "display.mode" and "test.mode"). You can also specify width and height parameters which provide a hint to the embedding environment about the ideal height/width for the app.

enableBookmarking

Can be one of "url", "server", or "disable". The default value, NULL, will respect the setting from any previous calls to enableBookmarking(). See enableBookmarking() for more information on bookmarking your app.

uiPattern

A regular expression that will be applied to each GET request to determine whether the ui should be used to handle the request. Note that the entire request path must match the regular expression in order for the match to be considered successful.

launch.browser

Logical; if TRUE (default) open in an external browser even when running inside RStudio. If FALSE, use RStudio Viewer (when available).

...

arguments passed into golem options via with_golem_options(). See ?golem::get_golem_options for details.

Package 'shinymrp'

Help Index

Return example MRPModel object with estimation results.

Description

Usage

Arguments

Value

Return example poststratification data

Description

Usage

Value

Return example data

Description

Usage

Arguments

Value

Create a new MRPWorkflow object

Description

Usage

Value

MRPModel objects

Description

Arguments

Value

Methods

Data access

Model fitting

Posterior summary & diagnostics

Post-processing

Saving model object

Examples

Check if poststratification has been performed

Description

Usage

Value

Check if model has been fitted

Description

Usage

Value

Return sampling diagnostics

Description

Usage

Arguments

Value

Fit multilevel regression model using cmdstanr

Description

Usage

Arguments

Value

Return model formula

Description

Usage

Value

Create inputs for leave-one-out cross-validation

Description

Usage

Value

Return model metadata.

Description

Usage

Value

Return model specification

Description

Usage

Value

Run poststratification to generate population estimates

Description

Usage

Arguments

Value

Create input for posterior predictive check

Description

Usage

Value

Save model object to file

Description

Usage

Arguments

Value

Return model Stan code.

Return example `MRPModel` object with estimation results.