This function uses the full posterior distributions of parameters estimated
by jointModel() 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.
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.
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 credible interval is a univariate numeric value greater than 0 and less than 1.
Input model fit contains p10 parameter.
If model fit contains alpha, cov.val must be provided.
Input cov.val is numeric.
Input cov.val is the same length as the number of estimated covariates.
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
# \donttest{
# Ex. 1: Calculating mu_critical with site-level covariates
# Load data
data(gobyData)
# Fit a model including 'Filter_time' and 'Salinity' site-level covariates
fit.cov <- jointModel(data = gobyData, cov = c('Filter_time','Salinity'),
family = "poisson", p10priors = c(1,20), q = FALSE,
multicore = FALSE)
#>
#> SAMPLING FOR MODEL 'joint_binary_cov_pois' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 4.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 1: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 1: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 1: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 1: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 1: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 1: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 1: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.711 seconds (Warm-up)
#> Chain 1: 1.719 seconds (Sampling)
#> Chain 1: 2.43 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'joint_binary_cov_pois' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 4.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 2: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 2: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 2: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 2: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 2: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 2: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 2: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 6.416 seconds (Warm-up)
#> Chain 2: 1.769 seconds (Sampling)
#> Chain 2: 8.185 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'joint_binary_cov_pois' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 4.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 3: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 3: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 3: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 3: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 3: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 3: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 3: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.721 seconds (Warm-up)
#> Chain 3: 1.744 seconds (Sampling)
#> Chain 3: 2.465 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'joint_binary_cov_pois' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 4.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 4: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 4: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 4: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 4: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 4: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 4: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 4: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.597 seconds (Warm-up)
#> Chain 4: 1.704 seconds (Sampling)
#> Chain 4: 2.301 seconds (Total)
#> Chain 4:
#> Refer to the eDNAjoint guide for visualization tips: https://bookdown.org/abigailkeller/eDNAjoint_vignette/tips.html#visualization-tips
# Calculate mu_critical at the mean covariate values (covariates are
# standardized, so mean = 0)
muCritical(fit.cov$model, cov.val = c(0,0), ci = 0.9)
#> $median
#> [1] 0.005256258
#>
#> $lower_ci
#> Highest Density Interval: 1.80e-03
#>
#> $upper_ci
#> Highest Density Interval: 9.79e-03
#>
# Calculate mu_critical at habitat size 0.5 z-scores greater than the mean
muCritical(fit.cov$model, cov.val = c(0,0.5), ci = 0.9)
#> $median
#> [1] 0.004406221
#>
#> $lower_ci
#> Highest Density Interval: 1.39e-03
#>
#> $upper_ci
#> Highest Density Interval: 8.16e-03
#>
# Ex. 2: Calculating mu_critical with multiple traditional gear types
# Load data
data(greencrabData)
# Fit a model with no site-level covariates
fit.q <- jointModel(data = greencrabData, cov = NULL, family = "negbin",
p10priors = c(1,20), q = TRUE, multicore = FALSE)
#>
#> SAMPLING FOR MODEL 'joint_binary_catchability_negbin' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000393 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.93 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 1: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 1: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 1: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 1: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 1: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 1: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 1: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 3.681 seconds (Warm-up)
#> Chain 1: 11.752 seconds (Sampling)
#> Chain 1: 15.433 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'joint_binary_catchability_negbin' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000319 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 3.19 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 2: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 2: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 2: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 2: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 2: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 2: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 2: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 3.339 seconds (Warm-up)
#> Chain 2: 11.578 seconds (Sampling)
#> Chain 2: 14.917 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'joint_binary_catchability_negbin' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000321 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 3.21 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 3: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 3: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 3: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 3: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 3: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 3: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 3: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 3.429 seconds (Warm-up)
#> Chain 3: 10.992 seconds (Sampling)
#> Chain 3: 14.421 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'joint_binary_catchability_negbin' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 0.000343 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 3.43 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 4: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 4: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 4: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 4: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 4: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 4: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 4: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 3.449 seconds (Warm-up)
#> Chain 4: 8.828 seconds (Sampling)
#> Chain 4: 12.277 seconds (Total)
#> Chain 4:
#> Refer to the eDNAjoint guide for visualization tips: https://bookdown.org/abigailkeller/eDNAjoint_vignette/tips.html#visualization-tips
# Calculate mu_critical
muCritical(fit.q$model, cov.val = NULL, ci = 0.9)
#> gear_1 gear_2
#> median 0.059070936 0.046617182
#> lower_ci 0.009265172 0.006740918
#> upper_ci 0.135199672 0.104594860
# }