joint_summarize: Summarize posterior distributions of model parameters.

In eDNAjoint: Joint Modeling of Traditional and Environmental DNA Survey Data in a Bayesian Framework

Summarize posterior distributions of model parameters.

Description

This function summarizes the posterior distributions of specified parameters from a model fit. Summary includes mean, sd, and specified quantiles, as well as effective sample size (n_eff) and Rhat for estimated parameters. See more examples in the Package Vignette.

Usage

joint_summarize(model_fit, par = "all", probs = c(0.025, 0.975), digits = 3)

Arguments

model_fit	An object of class stanfit.
par	A character vector of parameter names. The default is 'all'.
probs	A numeric vector of quantiles of interest. The default is c(0.025,0.975).
digits	An integer indicating the number of decimal places to round values in summary table. Default value is 3.

Value

A summary table of parameter estimates.

Note

Before fitting the model, this function checks to ensure that the function is possible given the inputs. These checks include:

• Input model fit is an object of class 'stanfit'.

• Input probs is a numeric vector.

• Input par is a character vector.

• Input par are present in fitted model.

• Input model fit has converged (i.e. no divergent transitions after warm-up).

If any of these checks fail, the function returns an error message.

Examples

data(green_crab_data)

# Fit a model
model_fit <- joint_model(data = green_crab_data, family = "negbin", q = TRUE,
                         multicore = FALSE)

# Create summary table of all parameters
joint_summarize(model_fit$model)

# Summarize just 'p10' parameter
joint_summarize(model_fit$model, par = "p10", probs = c(0.025, 0.975),
                digits = 3)

eDNAjoint documentation built on June 21, 2025, 9:08 a.m.