prepare_data: Wrangle data to use for modelling input

In BrandonEdwards/bbsBayes: Hierarchical Bayesian Analysis of North American BBS Data

Wrangle data to use for modelling input

Description

prepare_data subsets raw BBS data by selected species and and wrangles stratified data for use as input for models.

Usage

prepare_data(
  strat_data = NULL,
  species_to_run = NULL,
  model = NULL,
  heavy_tailed = FALSE,
  n_knots = NULL,
  min_year = NULL,
  max_year = NULL,
  min_n_routes = 3,
  min_max_route_years = 3,
  min_mean_route_years = 1,
  strata_rem = NULL,
  quiet = FALSE,
  sampler = "jags",
  basis = "original",
  ...
)

Arguments

strat_data	Large list of stratified data returned by stratify()
species_to_run	Character string of the English name of the species to run
model	Character string of model to be used. Options are "slope", "firstdiff", "gam", "gamye.
heavy_tailed	Logical indicating whether the extra-Poisson error distribution should be modeled as a t-distribution, with heavier tails than the standard normal distribution. Default is currently FALSE, but recent results suggest users should strongly consider setting this to TRUE, even though it requires much longer convergence times
n_knots	Number of knots to be used in GAM function
min_year	Minimum year to keep in analysis
max_year	Maximum year to keep in analysis
min_n_routes	Minimum routes per strata where species has been observed. Defaults to 3
min_max_route_years	Minimum number of years with non-zero observations of species on at least 1 route. Defaults to 3
min_mean_route_years	Minimum average of years per route with the species observed. Defaults to 1.
strata_rem	Strata to remove from analysis. Defaults to NULL
quiet	Should progress bars be suppressed?
sampler	Which MCMC sampling software to use. Currently bbsBayes only supports "jags".
basis	Which version of the basis-function to use for the GAM smooth, the default is "original" the same basis used in Smith and Edwards 2020 and "mgcv" is an alternate that uses the "tp" basis from the packages mgcv (also used in brms, and rstanarm). If using the "mgcv" option, the user may want to consider adjusting the prior distributions for the parameters and their precision
...	Additional arguments

Value

List of data to be used for modelling, including:

model	The model to be used
heavy_tailed	Logical indicating whether the extra-Poisson error distribution should be modeled as a t-distribution
min_nu	if heavy_tailed is TRUE, minimum value for truncated gamma on DF of t-distribution noise default is 0 and user must change manually after function is run
ncounts	The number of counts containing useful data for the species
nstrata	The number of strata used in the analysis
ymin	Minimum year used
ymax	Maximum year used
nonzeroweight	Proportion of routes in each strata with species obervation
count	Vector of counts for the species
strat	Vector of strata to be used in the analysis
obser	Vector of unique observer-route pairings
year	Vector of years for each count
firstyr	Vector of indicator variables as to whether an observer was a first year
month	vector of numeric month of observation
day	vector of numeric day of observation
nobservers	Total number of observer-route pairings
fixedyear	Median of all years (ymin:ymax), included only with slope and firstdiff models
nknots	Number of knots to use for smooting functions, included only with GAM
X.basis	Basis function for n smoothing functions, included only with GAM

Examples

# Toy example with Pacific Wren sample data
# First, stratify the sample data

strat_data <- stratify(by = "bbs_cws", sample_data = TRUE)

# Prepare the stratified data for use in a model. In this
#   toy example, we will set the minimum year as 2009 and
#   maximum year as 2018, effectively only setting up to
#   model 10 years of data. We will use the "first difference
#   model.
model_data <- prepare_data(strat_data = strat_data,
                           species_to_run = "Pacific Wren",
                           model = "firstdiff",
                           min_year = 2009,
                           max_year = 2018)

# You can also specify the GAM model, with an optional number of
# knots to use for the GAM basis.
# By default, the number of knots will be equal to the floor
# of the total unique years for the species / 4
model_data <- prepare_data(strat_data = strat_data,
                           species_to_run = "Pacific Wren",
                           model = "gam",
                           n_knots = 9)

BrandonEdwards/bbsBayes documentation built on March 3, 2023, 9:55 a.m.