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$save("model.qs")
# Load model later
loaded_model <- qs::qread("model.qs")stan_code()
Return model Stan code.
library(shinymrp)
# Load example data
example_model <- example_model()
# Get Stan code
stan_code <- example_model$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