get_discrete: Retrieve nimbleCode for discrete spatial capture-recapture...

In sitkensis22/localSCR: Supporting functions for advanced Bayesian spatial capture-recapture modeling using 'nimble'

Retrieve nimbleCode for discrete spatial capture-recapture models

Description

Creates model code using the nimbleCode function.

Usage

get_discrete(
  type = "marked",
  dim_y = NULL,
  occ_specific = FALSE,
  enc_dist = "binomial",
  sex_sigma = FALSE,
  trapsClustered = FALSE
)

Arguments

type	Specifies the type of discrete model for either "marked" (the default) or "unmarked" data sources.
dim_y	Either NULL (the default) or an integer of either 2 or 3 that define the dimensional format the encounter history data are in. Note that this input is not used when type = "unmarked".
occ_specific	Logical. If FALSE, the encounter rate will not include an occasion-specific loop in the detection function; otherwise, the model will include a for loop for occasions (K) in the detection function. Default is FALSE. Only applied when type = "unmarked".
enc_dist	Either "binomial" or "poisson". Default is "binomial".
sex_sigma	A logical value indicating whether the scaling parameter ('sigma') is sex-specific
trapsClustered	A logical value indicating if traps are clustered in arrays across the sampling area.

Details

This function provides templates that could be copied and easily modified to include further model complexity such as covariates explaining detection probability. These discrete models include different encounter probability distributions and sex-specific scaling parameters for marked and unmarked data sets.

Value

A nimbleCode object from the nimble package.

Author(s)

Daniel Eacker

Examples

# get discrete model for 2D marked encounter data, binomial encounter 
# distribution, non-sex-specific scaling parameter, and no clustering of traps
discrete_model_m = get_discrete(type="marked", dim_y = 2,enc_dist = "binomial",
                      sex_sigma = FALSE, trapsClustered = FALSE)

# inspect model
discrete_model_m

# get discrete model for unmarked encounter data, binomial encounter 
# distribution, non-sex-specific scaling parameter, and no clustering of traps
discrete_model_u = get_discrete(type="unmarked",occ_specific = FALSE,
           enc_dist = "binomial",sex_sigma = FALSE, trapsClustered = FALSE)

# inspect model
discrete_model_u

sitkensis22/localSCR documentation built on May 15, 2022, 5:26 p.m.