Create and Run a runjags Portalcasting Model

Using the runjags (Denwood 2016) package to produce JAGS-based forecasts.
fit_runjags: Wraps up the runjags model object preparation functions with the model running (run.jags function in the runjags (Denwood 2016) package) we use to run JAGS (Plummer 2003) models in portalcasting.
runjags_data, runjags_monitor, runjags_model, runjags_inits: Produce the model-specific components as named.
forecast.runjags: A convenience function for extracting existing forecasts from runjags objects and summarizing them into a "forecast"-class object.
runjags_controls: Combines the run.jags control parameters that users may be interested in changing with a few portalcasting-specific parameters into a control list for input into specific model functions.

Usage

runjags_inits(inits)

runjags_model(model_file)

runjags_monitors(monitors, metadata)

runjags_data(data_names, abundance, metadata, covariates)

fit_runjags(
  abundance,
  metadata,
  covariates,
  monitors,
  inits,
  model_file,
  data_names,
  control_runjags = runjags_controls()
)

# S3 method for class 'runjags'
forecast(object, h, level, nsamples, seed = NULL, ...)

runjags_controls(
  nchains = 4,
  adapt = 10000,
  burnin = 10000,
  sample = 10000,
  thin = 10,
  modules = "glm",
  method = "interruptible",
  factories = "",
  mutate = NA,
  silent_jags = FALSE
)

Arguments

inits: list of model parameter initializer functions. See prefab_models_controls.
model_file: character value of the model file name. See prefab_models_controls.
monitors: character vector of parameters to track. Forecasted observations and state variables are tracked automatically.
metadata: list of model control elements. See prepare_metadata.
data_names: character vector of data values to include in the data list. See prefab_models_controls.
abundance: Non-negative integer-conformable vector of rodent abundances to use in forecasting. See prepare_abundance.
covariates: data.frame of covariates used in modeling. See prepare_covariates.
control_runjags: list of controls for running runjags models. See runjags_controls. Optional. If not provided here, will be taken from the model controls list.
object: A runjags-class object with columns of "X" values (state variables) in the the mcmc element.
h: integer-conformable number of steps forward to include in the forecast.
level: numeric of the confidence level to use in summarizing the predictions.
nsamples: integer (or integer numeric) number of samples used to summarizing model output of sample-based estimates.
seed: A single integer-conformable value or NULL set in set.seed.
...: Additional parameters
nchains: Non-negative integer-conformable value of the number of parallel chains to use. See run.jags.
adapt: Non-negative integer-conformable value of the number of adaptation steps to use. See run.jags.
burnin: Non-negative integer-conformable value of the number of burnin steps to use. See run.jags.
sample: Non-negative integer-conformable value of the number of sampling steps to use. See run.jags.
thin: Positive integer-conformable value of the thinning interval to use. See run.jags.
modules: character vector of external modules to add to JAGS. See run.jags.
method: character value of the run.jags method to use. Options include "rjags", "simple", "interruptible", "parallel", "rjparallel", "background", "bgparallel", and "snow". See run.jags.
factories: character vector of factory modules to add to JAGS. See run.jags.
mutate: A function or list (with the first element being a function) used to add variables to the posterior chain (rather than throughout sampling). See run.jags.
silent_jags: logical value for quieting the output from the runjags function, including the underlying JAGS output.

Value

fit_runjags: An object of class "runjags" of model components. See run.jags.
runjags_data: A list of model-specific data for use in run.jags.
runjags_monitor: A vector of model-specific character values of parameters to track in run.jags.
runjags_model: A single character value of the JAGS model block for run.jags.
runjags_inits: A function that takes the argument data to produce chain-specific initial values for run.jags.
runjags_controls: A list of controls.
forecast.runjags: list with "forecast"-class with named elements including "mean", "lower", "upper", and "level".

References

Denwood, M. J. 2016. runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of Statistical Software, 71:9. URL.

Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs Sampling. Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). ISSN 1609-395X.

Examples

if (FALSE) { # \dontrun{
   main1 <- file.path(tempdir(), "runjags")

   setup_dir(main = main1)
   dataset    <- "all"
   species    <- "DM"
   model      <- "jags_RW"
   model_file <- gsub("'", "",
                      models_controls(main1)[[model]]$fit$full_model_file)
 
   abundance      <- prepare_abundance(main    = main1,
                                       dataset = dataset,
                                       species = species,
                                       model   = model)
   model_controls <- models_controls(main       = main1,
                                     models     = model)[[model]]
   metadata       <- read_metadata(main        = main1)
   newmoons       <- read_newmoons(main        = main1)                                        
   covariates     <- read_covariates(main      = main1)
   control_runjags <- runjags_controls(nchains = 3, 
                                       adapt   = 1e3, 
                                       burnin  = 1e3, 
                                       sample  = 1e3, 
                                       thin    = 1)
   data_names      <- c("count", "N", "log_mean_count")

   runjags_model(model_file = model_file)

   runjags_monitors(monitors = c("mu", "sigma"),
                    metadata = metadata)

   data <- runjags_data(data_names = data_names,
                        abundance  = abundance,
                        metadata   = metadata,
                        covariates = covariates)

   runjags_inits(inits = list(mu    = rnorm(1, mean = data$log_mean_count, sd = 0.1),
                              sigma = runif(1, min  = 0.01, max = 0.5)))

   fit <- fit_runjags(abundance       = abundance, 
                      metadata        = metadata,
                      covariates      = covariates, 
                      monitors        = c("mu", "sigma"), 
                      inits           = list(mu    = rnorm(1, data$log_mean_count, 0.1),
                                             sigma = runif(1, 0.01, 0.5)), 
                      model_file      = model_file,
                      data_names      = data_names,
                      control_runjags = control_runjags)
 
   forecast(object   = fit,  
            h        = metadata$time$lead_time_newmoons,   
            level    = metadata$confidence_level,   
            nsamples = metadata$nsamples)

   unlink(main1, recursive = TRUE)
} # }

Usage

Arguments

Value

References

See also

Examples