Specify and fit model using count data from traditional, non eDNA surveys

This function implements a Bayesian model that estimates expected species catch rate using count data from traditional, non eDNA surveys. When multiple traditional gear types are used, an optional variation allows estimation of gear scaling coefficients, which scale the catchability of gear types relative to the expected catch rate of a reference gear type. Model is implemented using Bayesian inference using the rstan package, which uses Hamiltonian Monte Carlo to simulate the posterior distributions. See more examples in the Package Vignette.

Usage

traditionalModel(
  data,
  family = "poisson",
  q = FALSE,
  phipriors = NULL,
  multicore = FALSE,
  initial_values = NULL,
  n.chain = 4,
  n.iter.burn = 500,
  n.iter.sample = 2500,
  thin = 1,
  adapt_delta = 0.9,
  verbose = TRUE,
  seed = NULL
)

Arguments

data: A list containing data necessary for model fitting. Valid tags are count and count.type. count is a matrix or data frame of traditional survey count data, with first dimension equal to the number of sites (i) and second dimension equal to the maximum number of traditional survey replicates at a given site (j). count.type is an optional matrix or data frame of integers indicating gear type (k) used in corresponding count data, with first dimension equal to the number of sites (i) and second dimension equal to the maximum number of traditional survey replicates at a given site (j). Values should be integers beginning with 1 (referring to the first gear type) to n (last gear type). Empty cells should be NA and will be removed during processing. Sites, i, should be consistent in all count data.
family: The distribution class used to model traditional survey count data. Options include poisson ('poisson'), negative binomial ('negbin'), and gamma ('gamma'). Default value is 'poisson'.
q: A logical value indicating whether to estimate gear scaling coefficients, q, for traditional survey gear types (TRUE) or to not estimate gear scaling coefficients, q, for traditional survey gear types (FALSE). Default value is FALSE.
phipriors: A numeric vector indicating gamma distribution hyperparameters (shape, rate) used as the prior distribution for phi, the overdispersion in the negative binomial distribution for traditional survey gear data. Used when family = 'negbin.' If family = 'negbin', then default vector is c(0.25,0.25), otherwise, default is NULL.
multicore: A logical value indicating whether to parallelize chains with multiple cores. Default is FALSE.
initial_values: A list of lists of initial values to use in MCMC. The length should equal the number of MCMC chains. Initial values can be provided for parameters: mu and q. If no initial values are provided, default random values are drawn.
n.chain: Number of MCMC chains. Default value is 4.
n.iter.burn: Number of warm-up MCMC iterations. Default value is 500.
n.iter.sample: Number of sampling MCMC iterations. Default value is 2500.
thin: A positive integer specifying the period for saving samples. Default value is 1.
adapt_delta: Numeric value between 0 and 1 indicating target average acceptance probability used in rstan::sampling. Default value is 0.9.
verbose: Logical value controlling the verbosity of output (i.e., warnings, messages, progress bar). Default is TRUE.
seed: A positive integer seed used for random number generation in MCMC. Default is NULL, which means the seed is generated from 1 to the maximum integer supported by R.

Value

A list of:

a model object of class stanfit returned by rstan::sampling
initial values used in MCMC

Note

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

All tags in data are valid (i.e., include count and count.type).
Number of sites in count and count type data are equal.
All data are numeric (i.e., integer or NA).
Empty data cells (NA) match in count and count.type.
family is 'poisson', 'negbin', or 'gamma'.
phipriors (if used) is a vector of two numeric values.

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

Examples

# \donttest{
# Load data
data(greencrabData)

# Examine data in list
# This function uses only traditional survey count data and optionally
# the count type data
names(greencrabData)
#> [1] "qPCR.N"     "qPCR.K"     "count"      "count.type"

# Note that the surveyed sites (rows) should match in the data
dim(greencrabData$count)[1]
#> [1] 20
dim(greencrabData$count.type)[1]
#> [1] 20

# Fit a model without estimating a gear scaling coefficient for traditional
# survey gear types.
# This model assumes all traditional survey methods have the same
# catchability.
# Count data is modeled using a poisson distribution.
fit.no.q <- traditionalModel(data = greencrabData, family = "poisson",
                             q = FALSE, phipriors = NULL, multicore = FALSE,
                             verbose = TRUE)
#> 
#> SAMPLING FOR MODEL 'traditional_pois' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 5.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.51 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: 0.66 seconds (Warm-up)
#> Chain 1:                1.913 seconds (Sampling)
#> Chain 1:                2.573 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'traditional_pois' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.49 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: 0.746 seconds (Warm-up)
#> Chain 2:                1.766 seconds (Sampling)
#> Chain 2:                2.512 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'traditional_pois' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.8e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.48 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: 0.66 seconds (Warm-up)
#> Chain 3:                1.97 seconds (Sampling)
#> Chain 3:                2.63 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'traditional_pois' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 4.9e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.49 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: 0.7 seconds (Warm-up)
#> Chain 4:                1.742 seconds (Sampling)
#> Chain 4:                2.442 seconds (Total)
#> Chain 4: 


# Fit a model estimating a gear scaling coefficient for traditional survey
# gear types.
# This model does not assume all traditional survey methods have the same
# catchability.
# Count data is modeled using a negative binomial distribution.
fit.q <- traditionalModel(data = greencrabData, family = "negbin", q = TRUE,
                          phipriors = c(0.25,0.25), multicore = FALSE,
                          initial_values = NULL, n.chain = 4,
                          n.iter.burn = 500, n.iter.sample = 2500, thin = 1,
                          adapt_delta = 0.9, verbose = TRUE, seed = 123)
#> 
#> SAMPLING FOR MODEL 'traditional_catchability_negbin' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000281 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.81 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: 3.588 seconds (Warm-up)
#> Chain 1:                7.932 seconds (Sampling)
#> Chain 1:                11.52 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'traditional_catchability_negbin' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000302 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 3.02 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: 3.67 seconds (Warm-up)
#> Chain 2:                9.864 seconds (Sampling)
#> Chain 2:                13.534 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'traditional_catchability_negbin' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000304 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 3.04 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: 3.59 seconds (Warm-up)
#> Chain 3:                8.397 seconds (Sampling)
#> Chain 3:                11.987 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'traditional_catchability_negbin' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000313 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 3.13 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: 3.322 seconds (Warm-up)
#> Chain 4:                7.617 seconds (Sampling)
#> Chain 4:                10.939 seconds (Total)
#> Chain 4: 
# }

Specify and fit model using count data from traditional, non eDNA surveys

Usage

Arguments

Value

Note

Examples

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