Package {ValidationExplorer}

Title: Simulation-Based Tools for Bioacoustic Study Design
Version: 0.1.1
Description: Many bioacoustic data workflows rely on manual review (i.e., validation) of a subset of call files to provide information to statistical models that account for misclassification by automated algorithms. Because manual review can be prohibitively expensive, simulation can be a valuable tool to aid the design of studies that use validation. This package provides user-friendly functions to reduce the programming burden of simulation studies that compare validation sampling designs. Simulations assume the count-detection model, which is a realistic model for bioacoustic data, especially for bats. For more information, see Oram et al. (2025) <doi:10.1214/25-AOAS2096>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: nimble, R (≥ 4.1.0)
Imports: coda, dplyr, ggplot2, magrittr, purrr, rlang, rstan, stringr, tibble, tidyr, viridis
LazyData: true
Suggests: testthat (≥ 3.0.0), withr
Config/testthat/edition: 3
Config/Needs/website: rmarkdown
URL: https://j-oram.github.io/ValidationExplorer/
BugReports: https://github.com/j-oram/ValidationExplorer/issues
NeedsCompilation: no
Packaged: 2026-06-01 14:12:58 UTC; jacoboram
Author: Jacob Oram ORCID iD [aut, cre, cph], Katharine Banner ORCID iD [aut], Christian Stratton ORCID iD [aut], Kathryn Irvine ORCID iD [aut]
Maintainer: Jacob Oram <jacoboram@montana.edu>
Repository: CRAN
Date/Publication: 2026-06-07 18:50:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Negation of %in%

Description

⁠%notin%⁠ checks if elements are not in a vector, acting as the opposite of %in%.

Usage

x %notin% table

Arguments

x

Vector of values to check

table

Vector to check against

Value

A logical vector indicating if there are values not in table.

Examples

1 %notin% c(2, 3, 4)   # TRUE
2 %notin% c(2, 3, 4)   # FALSE

ValExp_example_fit

Description

Draws from an MCMC algorithm fit to simulated data. This object exists solely for easing examples in documentation of mcmc_sum.

Format

A list of length 3 matrices with columns and rows as described here.

Details

Source

Simulated data using simulate_validatedData, then isolated one dataset and the corresponding zeros. Draws were obtained from model fitting using the code inside tune_mcmc.

Examples

str(ValExp_example_fit)

example_output

Description

A dataframe in the format output from running run_sims. This exists solely for the purpose of making documentation of visualization functions easier.

Format

A dataframe containing the columns described below.

Source

A complete small-scale simulation study was run using the functions in ValidationExplorer.

Examples


head(example_output)

visualize_parameter_group(
  example_output,
  pars = "lambda",
  theta_scenario = "1",
  scenarios = 1:2
)

example_val_sum: Example summaries of validated data

Description

Example output from summarize_n_validated.

Format

A dataframe with 2 rows and 4 columns

Details

Source

Data was simulated using simulate_validatedData and summarized using summarize_n_validated.

Examples

example_val_sum

Further masking of true species labels to mimic whether a recording is confirmable

Description

Further masking of true species labels to mimic whether a recording is confirmable

Usage

make_not_confirmable(masked_df, confirmable_limits)

Value

A copy of the original dataframe, with some selected observations masked because they could not be confirmed.

Mask a proportion of all visits: A function for simulating a fixed effort validation design.

Description

Mask a proportion of all visits: A function for simulating a fixed effort validation design.

Usage

mask_FE_all_visits(df, effort_prop, seed = NULL)

Arguments

df

A dataframe object in the format of full_dfs output from simulate_validatedData

effort_prop

The proportion of recordings to be randomly sampled from the first visit for validation

seed

An optional random seed to make masking reproducible

Value

A dataframe object that is a copy of the input df, but with the appropriate level of effort according to a fixed effort validation design.

Examples

library(dplyr)

cd_data <- sim_dat()$full_df %>% tidyr::uncount(weights = count, .remove = FALSE)
FE_data <- mask_FE_all_visits(cd_data, effort_prop = 0.1)

head(cd_data)
head(FE_data)

FE_data %>% 
  group_by(site, visit) %>% 
  summarize(
    prop_validated = sum(!is.na(true_spp))/n()
  )

mask_by_spp: simulate a validation design

Description

mask_by_spp: simulate a validation design

Usage

mask_by_spp(data, props_to_val)

Arguments

data

A dataframe containing the columns site, visit, true_spp, id_spp, count

props_to_val

a vector containing the proportion of recordings to validate for each species

Value

A list containing two elements: final_df and data_sum. final_df is a copy of the input data masked according to the validation design supplied by props_to_val. The second output, data_sum is a dataframe containing a summary of the number and proportion of ambiguous (i.e., not validated) recordings. It provides a check that the masking function is working correctly.

Examples

library(dplyr)

dat <- sim_dat()$full_df

head(dat)

dat <- dat %>% tidyr::uncount(weights = count, .remove = FALSE)
val_dat <- mask_by_spp(dat, props_to_val = c(rep(.1, 4), rep(.4, 4)))

val_dat$final_df %>% group_by(id_spp) %>%
  summarize(prop_vald = sum(!is.na(true_spp))/n())

MCMC_sum: A custom function for summarizing MCMC posterior sampling

Description

MCMC_sum: A custom function for summarizing MCMC posterior sampling

Usage

mcmc_sum(out, thin = 1, truth)

Arguments

out

Draws from a model fit using a probabilistic programming language (e.g., Stan, NIMBLE or JAGS). The expected format of this input is a list, where each entry is a Markov chain.

thin

An optional thinning interval.

truth

A vector with the true parameter values organized alphanumerically by parameter value (e.g., lambda\[1\], lambda\[2\], psi\[1\], psi\[2\], theta\[1,1\], theta\[1,2\], theta\[2,1\], theta\[2,2\])

Value

A dataframe object summarizing the MCMC draws, including diagnostics, quantiles and posterior means.

Examples


# An example fit of one dataset
draws <- ValExp_example_fit

# The data generating values
truth <- c(11,2,0.3, 0.6, 0.9, 0.15, 0.10, 0.85)

mcmc_sum(draws, truth = truth)

plot_bias_vs_calls: Compare validation designs based on estimation error and expected level of effort

Description

plot_bias_vs_calls: Compare validation designs based on estimation error and expected level of effort

Usage

plot_bias_vs_calls(
  sim_summary,
  calls_summary,
  pars = NULL,
  regex_pars = NULL,
  theta_scenario,
  scenarios,
  max_calls = NULL,
  convergence_threshold = 1.1
)

Arguments

sim_summary

Simulation summary from many datasets under many validation scenarios in the format output by mcmc_sum.

calls_summary

Summary of the validation design in the format as output from summarize_n_validated

pars

A character vector of parameters to visualize.

regex_pars

String containing the name of a group of parameters to visualize. Must be one of "lambda", "psi", or "theta".

theta_scenario

String containing the theta scenario ID that was used to run simulations in run_sims. This string must match between calls_summary and sim_summary.

scenarios

A vector of integers corresponding to the validation designs you would like to visualize.

max_calls

The maximum number of calls that can be manually reviewed. All points beyond this threshold will be shaded gray in the plot. Default value is NULL.

convergence_threshold

A threshold for the Gelman-Rubin statistic; values below this threshold indicate that a parameter has converged.

Value

A ggplot2 object showing the number of calls validated on the x-axis and the average estimation error on the y-axis.

Examples


sim_summary <- example_output
calls_summary <- example_val_sum

plot_bias_vs_calls(sim_summary, calls_summary, regex_pars = "lambda",
                   theta_scenario = "1", scenarios = 1:2,
                   convergence_threshold = 1.05)

plot_coverage_vs_calls: Compare validation designs based on coverage of 95% posterior intervals and expected level of effort

Description

plot_coverage_vs_calls: Compare validation designs based on coverage of 95% posterior intervals and expected level of effort

Usage

plot_coverage_vs_calls(
  sim_summary,
  calls_summary,
  pars = NULL,
  regex_pars = NULL,
  theta_scenario,
  scenarios,
  max_calls = NULL,
  convergence_threshold = 1.1
)

Arguments

sim_summary

Simulation summary from many datasets under many validation scenarios in the format output by mcmc_sum.

calls_summary

Summary of the validation design in the format as output from summarize_n_validated

pars

A character vector of parameters to visualize.

regex_pars

String containing the name of a group of parameters to visualize. Must be one of "lambda", "psi", or "theta".

theta_scenario

String containing the theta scenario ID that was used to run simulations in run_sims. This string must match between calls_summary and sim_summary.

scenarios

A vector of integers corresponding to the validation designs you would like to visualize.

max_calls

The maximum number of calls that can be manually reviewed. All points beyond this threshold will be shaded gray in the plot. Default value is NULL.

convergence_threshold

A threshold for the Gelman-Rubin statistic; values below this threshold (and near 1) indicate that a parameter has converged.

Value

A ggplot2 object showing the number of calls validated on the x-axis and the average coverage of 95% credible intervals on the y-axis.

Examples


sim_summary <- example_output
calls_summary <- example_val_sum

plot_coverage_vs_calls(sim_summary, calls_summary, regex_pars = "lambda",
                   theta_scenario = "1", scenarios = 1:2,
                   convergence_threshold = 1.05)

plot_width_vs_calls: Compare validation designs based on 95% posterior interval width and expected level of effort

Description

plot_width_vs_calls: Compare validation designs based on 95% posterior interval width and expected level of effort

Usage

plot_width_vs_calls(
  sim_summary,
  calls_summary,
  pars = NULL,
  regex_pars = NULL,
  theta_scenario,
  scenarios,
  max_calls = NULL,
  convergence_threshold = 1.1
)

Arguments

sim_summary

Simulation summary from many datasets under many validation scenarios in the format output by mcmc_sum.

calls_summary

Summary of the validation design in the format as output from summarize_n_validated

pars

A character vector of parameters to visualize.

regex_pars

String containing the name of a group of parameters to visualize. Must be one of "lambda", "psi", or "theta".

theta_scenario

String containing the theta scenario ID that was used to run simulations in run_sims. This string must match between calls_summary and sim_summary.

scenarios

A vector of integers corresponding to the validation designs you would like to visualize.

max_calls

The maximum number of calls that can be manually reviewed. All points beyond this threshold will be shaded gray in the plot. Default value is NULL.

convergence_threshold

A threshold for the Gelman-Rubin statistic; values below this threshold indicate that a parameter has converged.

Value

A ggplot2 object showing the number of calls validated on the x-axis and the average 95% credible interval on the y-axis.

Examples


sim_summary <- example_output
calls_summary <- example_val_sum

plot_width_vs_calls(sim_summary, calls_summary, regex_pars = "lambda",
                   theta_scenario = "1", scenarios = 1:2,
                   convergence_threshold = 1.05)

run_sims: conduct simulations easily

Description

run_sims: conduct simulations easily

Usage

run_sims(
  data_list,
  zeros_list,
  DGVs,
  theta_scenario_id,
  parallel = TRUE,
  niter = 2000,
  nburn = floor(niter/2),
  thin = 1,
  nchains = 3,
  save_fits = FALSE,
  save_individual_summaries_list = FALSE,
  directory = tempdir()
)

Arguments

data_list

nested list of masked dataframes (datasets nested within scenarios – this is the format of masked_dfs output from simulate_validatedData)

zeros_list

list of dataframes containing the site/visit/true_spp/id_spp combinations where no calls were observed.

DGVs

A named list with entries psi, lambda and theta containing the true values of the respective parameters.

theta_scenario_id

A character string ID for the simulations being run.

parallel

Should models be fit in parallel? Default value is TRUE.

niter

Number of iterations per MCMC chain.

nburn

Number of warmup iterations.

thin

Thinning interval for the MCMC chains.

nchains

The number of chains.

save_fits

Should individual model fits be saved? This could require large amounts of disk space if you are fitting many large models to big datasets. Default value is FALSE.

save_individual_summaries_list

Should summaries for individual model fits be saved? While this requires much less space than save_fits, we still recommend keeping this at the default value of FALSE. Only use it if you anticipate that simulations may be interrupted.

directory

The directory to save objects. Defaults to tempdir(), but users should specify a permanent location for real simulation studies as tempdir() is cleared at the end of the R session.

Value

a dataframe with the summaries (from mcmc_sum) for all scenarios and datasets. A copy of this output is also saved to the current working directory.

Examples

# :::::::::::: Simulate data ::::::::::::: #
psi <- c(0.3, 0.6)
lambda <- c(11, 2)

nspecies <- length(psi)
nsites <- 30
nvisits <- 5

test_theta1 <- matrix(c(0.9, 0.1, 0.15, 0.85), byrow = TRUE, nrow = 2)
val_scenarios <- list(spp1 = c(.75, .5), spp2 = .5)

td <- withr::local_tempdir()

fake_data <- simulate_validatedData(
  n_datasets = 5,
  design_type = "BySpecies",
  scenarios = val_scenarios,
  nsites = nsites,
  nvisits = nvisits,
  nspecies = nspecies,
  psi = psi,
  lambda = lambda,
  theta = test_theta1,
  save_datasets = FALSE,
  save_masked_datasets = FALSE,
  directory = td
)

# ::::::::::::: run simulations on sim'd data ::::::::::: #



out <- run_sims(
  data_list = fake_data$masked_dfs,
  zeros_list = fake_data$zeros,
  DGVs = list(lambda = lambda, psi = psi, theta = test_theta1),
  theta_scenario_id = 'StratBySpecies_1',
  parallel = FALSE,
  nchains = 2,
  niter = 500,
  nburn = 250,
  thin = 1,
  save_fits = FALSE,
  save_individual_summaries_list = FALSE,
  directory = td
)

Simulate data from the count-detection model with counts per site-visit

Description

Simulate data from the count-detection model with counts per site-visit

Usage

sim_dat(
  nsites = 100,
  nspecies = 8,
  nvisits = 4,
  seed = NULL,
  psi = stats::runif(nspecies, 0.4, 0.9),
  lambda = abs(stats::rnorm(nspecies, 0, 100)),
  theta = t(apply(18 * diag(nspecies) + 2, 1, function(x) nimble::rdirch(1, x)))
)

Arguments

nsites

the number of sites assumed in the design. Default value is 100.

nspecies

the number of species in the assemblage. Default is 8.

nvisits

the number of visits (detector nights) assumed for each site. Default is 4.

seed

optional seed if you would like to reproduce the data simulation.

psi

a vector of length nspecies that contains the occurrence probabilities for each species in the assemblage. These values must be in \[0,1\]. Default is to draw a random vector from a U(.4, .9) distribution.

lambda

vector of length nspecies that contains the relative activity parameters for each species. Note these values need to be positive. By default, lambda values are the absolute value of normal(0, 100) random variables.

theta

n nspecies x nspecies matrix containing the (mis)classification probabilities for each species. All entries must be in (0,1], with the rows of the matrix summing to 1. The default draws rows from a dirichlet distribution with concentrations determined by location in the matrix (diagonal values have higher concentrations).

Value

A list containing full_df, a complete dataframe simulated under the user's specified parameter settings. et_sim_datasets.R. The second list element is params, the parameters used to simulate data in list form.

Examples

fake_data <- sim_dat(
   nsites = 30,
   nspecies = 2,
   nvisits = 3,
   seed = 101,
   psi = c(.3, .6),
   lambda = c(8, 3)
)

head (fake_data$full_df)

Simulate many datasets under candidate validation designs

Description

Simulate many datasets under candidate validation designs

Usage

simulate_validatedData(
  n_datasets,
  design_type = c("BySpecies", "FixedPercent"),
  scenarios = NULL,
  nsites = 100,
  nspecies = 8,
  nvisits = 3,
  psi = runif(nspecies, 0.1, 0.9),
  lambda = abs(rnorm(nspecies, 0, 5)),
  theta = t(apply(diag(18, nrow = nspecies) + 2, 1, function(x) {
    
    nimble::rdirch(alpha = x)
 })),
  confirmable_limits = NULL,
  scen_expand = TRUE,
  scen_df = NULL,
  save_datasets = FALSE,
  save_masked_datasets = FALSE,
  directory = tempdir()
)

Arguments

n_datasets

The number of datasets you would like to have simulated. Each of these simulated datasets will be subjected to all candidate validation designs.

design_type

Character string, either "BySpecies" for a stratified-by-species design, or "FixedPercentage" for a fixed effort design (see Oram et al., in review for more details on each of these)

scenarios

if design_type = "BySpecies", the scenarios argument must be a list with each entry corresponding to the potential levels of effort for a particular autoID label. If design_type == "FixedPercent", then the scenarios argument must be a vector with each entry corresponding to a potential percent of calls to be sampled from the first visit at each site. See vignette for an example.

nsites

number of sites in each dataset

nspecies

size of the species assemblage

nvisits

the number of visits to each site. Note that these simulations assume a balanced design.

psi

a vector of length nspecies with the assumed occurrence probabilities for each species

lambda

a vector of length nspecies with the assumed relative activity levels for each species. Make sure the order is correct and matches psi.

theta

a matrix containing the (mis)classification probabilities. The rows of this matrix must sum to 1. See vignette for an example.

confirmable_limits

A numeric vector containing the lower and and upper bounds on the site-visit probabilities that a recording can be validated ("confirmed").

scen_expand

If design_type = "BySpecies", should simulate_validatedData expand the list of scenarios? If TRUE (the default value), then scenarios must be a list; if FALSE, then simulate_validatedData expects a user-supplied dataframe object through the scen_df argument.

scen_df

If scen_expand = FALSE, a user-supplied dataframe object with each row corresponding to the validation scenario and each column to the species. Default value is NULL.

save_datasets

logical. If TRUE, the datasets without any masking of true species labels (i.e., corresponding to complete validation of all recordings) will be saved. Default value is FALSE.

save_masked_datasets

logical. If TRUE, the masked datasets (i.e., the simulated datasets with partial validation according to the simulation scenario) will be saved. This means that there will be n_datasets x nrow(scenarios_dataframe) datasets saved: one for each dataset under each validation scenario. Default value is FALSE.

directory

character. Required if save_datasets = TRUE or save_masked_datasets = TRUE. This is where the datasets will be saved. By default, a temporary directory will be used. This must be changed if access to saved datasets is desired after the end of the R session, as tempdir() is cleared at the end of the session.

Value

A list containing three elements:

  1. full_datasets: A list of length n_datasets with unmasked datasets (i.e., full validation of all recordings). If save_datasets = TRUE, then these will be saved individually in directory as dataset_n.rds, where n is the dataset number.

  2. zeros: A list of length n_datasets containing all of the site-visits where no recordings of a certain classification were observed. For example, if, in dataset 10, there were no calls from species 1 that were classified as 3 on visit 4 to site 156, then the 10th entry of this list would contain a dataset with a row corresponding tosite = 156, visit = 4, true_spp = 1, id_spp = 3, with count = 0. These zeros are necessary for housekeeping in the model-fitting process. If save_datasets = TRUE, the zeros for each each dataset will be saved in directory individually as zeros_in_dataset_n.rds, where n is the dataset number.

  3. masked_dfs: A nested list containing each dataset masked under each scenario. masked_dfs\[\[9\]\]\[\[27\]\] contains dataset 27, assuming validation scenario 9. If save_masked_datasets = TRUE, then each dataset/scenario scenario combination is saved individually in directory as dataset_n_masked_under_scenario_s.rds, where n is the dataset number and s is the scenario number.

Examples


psi <- c(0.3, 0.6)
lambda <- c(11, 2)

nspecies <- length(psi)
nsites <- 30
nvisits <- 5

test_theta1 <- matrix(c(0.9, 0.1, 0.15, 0.85), byrow = TRUE, nrow = 2)
val_scenarios <- list(spp1 = c(.75, .5), spp2 = .5)

fake_data <- simulate_validatedData(
  n_datasets = 5,
  design_type = "BySpecies",
  scenarios = val_scenarios,
  nsites = nsites,
  nvisits = nvisits,
  nspecies = nspecies,
  psi = psi,
  lambda = lambda,
  theta = test_theta1,
  save_datasets = FALSE,
  save_masked_datasets = FALSE,
  directory = tempdir()
)

Summarize the number of validated recordings

Description

Summarize the number of validated recordings

Usage

summarize_n_validated(data_list, scenario_numbers, theta_scenario)

Arguments

data_list

A list of masked datasets in the format output from simulate_validatedData.

scenario_numbers

An integer vector denoting the scenarios to be summarized.

theta_scenario

The identifier for the classifier scenario as a character string.

Value

A dataframe object that contains the theta scenario, validation scenario, and number of validated calls.

Examples


psi <- c(0.3, 0.6)
lambda <- c(11, 2)

nspecies <- length(psi)
nsites <- 30
nvisits <- 5

test_theta1 <- matrix(c(0.9, 0.1, 0.15, 0.85), byrow = TRUE, nrow = 2)
val_scenarios <- list(spp1 = c(.75, .5), spp2 = .5)

fake_data <- simulate_validatedData(
  n_datasets = 5,
  design_type = "BySpecies",
  scenarios = val_scenarios,
  nsites = nsites,
  nvisits = nvisits,
  nspecies = nspecies,
  psi = psi,
  lambda = lambda,
  theta = test_theta1,
  save_datasets = FALSE,
  save_masked_datasets = FALSE,
  directory = withr::local_tempdir()
)

summarize_n_validated(
  data_list = fake_data$masked_dfs,
  scenario_numbers = 1:nrow(fake_data$scenarios_df),
  theta_scenario = '1'
)

Get suggested MCMC settings prior to starting your simulations

Description

Get suggested MCMC settings prior to starting your simulations

Usage

tune_mcmc(dataset, zeros, return_fit = TRUE)

Arguments

dataset

A dataframe containing the validated and ambiguous data to be fit. Expected format is that of a single masked dataframe contained in the output from simulate_validatedData. We recommend using a dataset from your lowest-effort validation scenario.

zeros

A dataframe containing the site/visit/true_spp/id_spp combinations that were never observed (count = 0). This will be one of the elements of the zeros object ouput from simulate_validatedData.

return_fit

A logical indicating whether the draws from the posterior should be returned. Default value is set to TRUE to encourage visual inspection of chains during the tuning process.

Value

A list containing the expected time to fit a single dataset with 10,000 iterations per chain, the draws for each chain, a guess for the minimum number of iterations and warmup required to to ensure Rhat <= 1.1 for all model parameters, and a dataframe containing effective sample sizes for each parameter.

Examples


psi <- c(0.3, 0.6)
lambda <- c(11, 2)

nspecies <- length(psi)
nsites <- 30
nvisits <- 5

test_theta1 <- matrix(c(0.9, 0.1, 0.15, 0.85), byrow = TRUE, nrow = 2)
val_scenarios <- list(spp1 = c(.75, .5), spp2 = .5)

fake_data <- simulate_validatedData(
  n_datasets = 5,
  design_type = "BySpecies",
  scenarios = val_scenarios,
  nsites = nsites,
  nvisits = nvisits,
  nspecies = nspecies,
  psi = psi,
  lambda = lambda,
  theta = test_theta1,
  save_datasets = FALSE,
  save_masked_datasets = FALSE,
  directory = withr::local_tempdir()
)
# scenario 1 has the lowest effort, so use the 5th dataset from that scenario
# Not run during checks
if (interactive()) {
# note the index of the zeros matches the index of the dataset
tune_mcmc(dataset = fake_data$masked_dfs[[1]][[5]], zeros = fake_data$zeros[[5]])
}

visualize_parameter_group

Description

Visualize simulation results for entire groups of parameters

Usage

visualize_parameter_group(
  sim_summary,
  pars,
  theta_scenario,
  scenarios,
  convergence_threshold = 1.1
)

Arguments

sim_summary

Summary output in the format of run_sims().

pars

The parameters to be visualized.

theta_scenario

The theta scenario IDs. This should match the theta_scenario_id argument ofrun_sims().

scenarios

Scenarios to be visualized.

convergence_threshold

If the Gelman-Rubin statistic is below this value, consider an MCMC to have converged. Default value is 1.1, but we recommend 1.05.

Value

A ggplot object summarizing simulation results.

Examples


visualize_parameter_group(
  example_output,
  pars = "lambda",
  theta_scenario = "1",
  scenarios = 1:2
)

visualize_single_parameter

Description

See detailed simulation study results for a parameter of interest

Usage

visualize_single_parameter(
  sim_summary,
  par,
  theta_scenario,
  scenarios,
  convergence_threshold = 1.1
)

Arguments

sim_summary

Summary output in the format of run_sims.

par

The parameter to be visualized.

theta_scenario

The theta scenario IDs. This should match the theta_scenario_id argument ofrun_sims().

scenarios

Scenarios to be visualized.

convergence_threshold

If the Gelman-Rubin statistic is below this value, consider an MCMC to have converged. Default value is 1.1, but we recommend 1.05.

Value

A ggplot object summarizing simulation results.

Examples


visualize_single_parameter(
  example_output,
  par = "lambda[1]",
  theta_scenario = "1",
  scenarios = 1:2,
  convergence_threshold = 1.05
)