eDNAjoint (version 0.3.3)

mu_critical: Calculate mu_critical

Description

This function uses the full posterior distributions of parameters estimated by joint_model() to calculate mu_critical, or the expected catch rate at which the probabilities of a false positive eDNA detection and true positive eDNA detection are equal. See more examples in the Package Vignette.

Usage

mu_critical(model_fit, cov_val = NULL, ci = 0.9)

Value

A list with median mu_critical and lower and upper bounds on the credible interval. If multiple gear types are used, a table of mu_critical and lower and upper credible interval bounds is returned with one column for each gear type.

Arguments

model_fit

An object of class stanfit

cov_val

A numeric vector indicating the values of site-level covariates to use for prediction. Default is NULL.

ci

Credible interval calculated using highest density interval (HDI). Default is 0.9 (i.e., 90% credible interval).

Examples

# \donttest{
# Ex. 1: Calculating mu_critical with site-level covariates

# Load data
data(goby_data)

# Fit a model including 'Filter_time' and 'Salinity' site-level covariates
fit_cov <- joint_model(data = goby_data, cov = c('Filter_time','Salinity'),
                       family = "poisson", p10_priors = c(1,20), q = FALSE,
                       multicore = FALSE)

# Calculate mu_critical at the mean covariate values (covariates are
# standardized, so mean = 0)
mu_critical(fit_cov$model, cov_val = c(0,0), ci = 0.9)

# Calculate mu_critical at habitat size 0.5 z-scores greater than the mean
mu_critical(fit_cov$model, cov_val = c(0,0.5), ci = 0.9)

# Ex. 2: Calculating mu_critical with multiple traditional gear types

# Load data
data(green_crab_data)

# Fit a model with no site-level covariates
fit_q <- joint_model(data = green_crab_data, cov = NULL, family = "negbin",
                     p10_priors = c(1,20), q = TRUE, multicore = FALSE)

# Calculate mu_critical
mu_critical(fit_q$model, cov_val = NULL, ci = 0.9)
# }