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Calculate the survey effort necessary to detect species presence, given the species expected catch rate.

Source: R/detection_calculate.R
detection_calculate.Rd

This function calculates the number of survey effort units to necessary detect species presence using median estimated parameter values from joint_model(). Detecting species presence is defined as producing at least one true positive eDNA detection or catching at least one individual. See more examples in the Package Vignette.

Usage

detection_calculate(
  model_fit,
  mu,
  cov_val = NULL,
  probability = 0.9,
  pcr_n = 3
)

Arguments

model_fit

An object of class stanfit.

mu

A numeric vector of species densities/capture rates. If multiple traditional gear types are represented in the model, mu is the catch rate of gear type 1.

cov_val

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

probability

A numeric value indicating the probability of detecting presence. The default is 0.9.

pcr_n

An integer indicating the number of PCR replicates per eDNA sample. The default is 3.

Value

A summary table of survey efforts necessary to detect species presence, given mu, for each survey 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 mu is a numeric vector.

  • Input probability is a univariate numeric value.

  • 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 necessary effort for detection 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)
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 5.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.53 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: 0.356 seconds (Warm-up)
#> Chain 1:                0.875 seconds (Sampling)
#> Chain 1:                1.231 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3.2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.32 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: 0.393 seconds (Warm-up)
#> Chain 2:                0.871 seconds (Sampling)
#> Chain 2:                1.264 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.26 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: 0.476 seconds (Warm-up)
#> Chain 3:                0.873 seconds (Sampling)
#> Chain 3:                1.349 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3.2e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.32 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: 0.37 seconds (Warm-up)
#> Chain 4:                0.867 seconds (Sampling)
#> Chain 4:                1.237 seconds (Total)
#> Chain 4: 
#> Refer to the eDNAjoint guide for visualization tips:  https://ednajoint.netlify.app/tips#visualization-tips 

# Calculate at the mean covariate values
# (covariates are standardized, so mean = 0)
detection_calculate(fit_cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
                    cov_val = c(0,0), pcr_n = 3)
#>        mu n_traditional n_eDNA
#>  [1,] 0.1            24     14
#>  [2,] 0.2            12      7
#>  [3,] 0.3             8      5
#>  [4,] 0.4             6      4
#>  [5,] 0.5             5      4
#>  [6,] 0.6             4      3
#>  [7,] 0.7             4      3
#>  [8,] 0.8             3      3
#>  [9,] 0.9             3      2
#> [10,] 1.0             3      2

# Calculate mu_critical at salinity 0.5 z-scores greater than the mean
detection_calculate(fit_cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
                    cov_val = c(0,0.5), pcr_n = 3)
#>        mu n_traditional n_eDNA
#>  [1,] 0.1            24     12
#>  [2,] 0.2            12      6
#>  [3,] 0.3             8      5
#>  [4,] 0.4             6      4
#>  [5,] 0.5             5      3
#>  [6,] 0.6             4      3
#>  [7,] 0.7             4      2
#>  [8,] 0.8             3      2
#>  [9,] 0.9             3      2
#> [10,] 1.0             3      2

# Ex. 2: Calculating necessary effort for detection 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)
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000488 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.88 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: 4.735 seconds (Warm-up)
#> Chain 1:                12.821 seconds (Sampling)
#> Chain 1:                17.556 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000397 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 3.97 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: 5.006 seconds (Warm-up)
#> Chain 2:                14.133 seconds (Sampling)
#> Chain 2:                19.139 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000397 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 3.97 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: 4.773 seconds (Warm-up)
#> Chain 3:                14.321 seconds (Sampling)
#> Chain 3:                19.094 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000402 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 4.02 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: 4.926 seconds (Warm-up)
#> Chain 4:                9.658 seconds (Sampling)
#> Chain 4:                14.584 seconds (Total)
#> Chain 4: 
#> Refer to the eDNAjoint guide for visualization tips:  https://ednajoint.netlify.app/tips#visualization-tips 

# Calculate
detection_calculate(fit_q$model, mu = seq(from = 0.1, to = 1, by = 0.1),
                    cov_val = NULL, pcr_n = 3)
#>        mu n_traditional_1 n_traditional_2 n_eDNA
#>  [1,] 0.1              25              32     28
#>  [2,] 0.2              13              17     15
#>  [3,] 0.3               9              12     10
#>  [4,] 0.4               7               9      8
#>  [5,] 0.5               6               8      6
#>  [6,] 0.6               6               7      5
#>  [7,] 0.7               5               6      5
#>  [8,] 0.8               5               5      4
#>  [9,] 0.9               4               5      4
#> [10,] 1.0               4               5      4

# Change probability of detecting presence to 0.95
detection_calculate(fit_q$model, mu = 0.1, cov_val = NULL,
                    probability = 0.95, pcr_n = 3)
#>       mu n_traditional_1 n_traditional_2 n_eDNA
#> [1,] 0.1              32              41     37
# }