tidy_mcmc_sampling: Tidy MCMC Sampling

In TidyDensity: Functions for Tidy Analysis and Generation of Random Data

Tidy MCMC Sampling

Description

This function performs Markov Chain Monte Carlo (MCMC) sampling on the input data and returns tidy data and a plot representing the results.

Usage

tidy_mcmc_sampling(.x, .fns = "mean", .cum_fns = "cmean", .num_sims = 2000)

Arguments

.x	The data vector for MCMC sampling.
.fns	The function(s) to apply to each MCMC sample. Default is "mean".
.cum_fns	The function(s) to apply to the cumulative MCMC samples. Default is "cmean".
.num_sims	The number of simulations. Default is 2000.

Details

Perform MCMC sampling and return tidy data and a plot.

The function takes a data vector as input and performs MCMC sampling with the specified number of simulations. It applies user-defined functions to each MCMC sample and to the cumulative MCMC samples. The resulting data is formatted in a tidy format, suitable for further analysis. Additionally, a plot is generated to visualize the MCMC samples and cumulative statistics.

Value

A list containing tidy data and a plot.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Utility: check_duplicate_rows(), convert_to_ts(), quantile_normalize(), util_beta_aic(), util_binomial_aic(), util_cauchy_aic(), util_chisq_aic(), util_exponential_aic(), util_f_aic(), util_gamma_aic(), util_generalized_beta_aic(), util_generalized_pareto_aic(), util_geometric_aic(), util_hypergeometric_aic(), util_inverse_burr_aic(), util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), util_negative_binomial_aic(), util_normal_aic(), util_paralogistic_aic(), util_pareto1_aic(), util_pareto_aic(), util_poisson_aic(), util_t_aic(), util_triangular_aic(), util_uniform_aic(), util_weibull_aic(), util_zero_truncated_binomial_aic(), util_zero_truncated_geometric_aic(), util_zero_truncated_negative_binomial_aic(), util_zero_truncated_poisson_aic()

Examples

# Generate MCMC samples
set.seed(123)
data <- rnorm(100)
result <- tidy_mcmc_sampling(data, "median", "cmedian", 500)
result

TidyDensity documentation built on May 29, 2024, 11:06 a.m.