Fit wisp to count data — wisp • wispack

This function takes a data frame of wisp variables (as columns) and fits a wisp model to it. Statistical analyses and plots are generated from the fitted model.

Usage

wisp(
  count.data,
  variables = list(),
  fit_only = FALSE,
  use.median = FALSE,
  bootstraps.num = 0,
  converged.resamples.only = TRUE,
  max.fork = 1,
  verbose = TRUE,
  model.settings = list(),
  MCMC.settings = list(),
  plot.settings = list()
)

Arguments

count.data: Data.frame, data to be modeled, with columns for model variables (count, bin, context, species, ran, fixedeffects), or equivalent variables as specified in the variables argument.
variables: List, names of the columns in count.data that correspond to the model variables. The list should contain only (but not necessarily all) named elements: count, bin, context, species, ran, and fixedeffects.
fit_only: Logical, if TRUE, only fits the model to the full data set using L-BFGS and returns the fitted model without running MCMC or bootstrapping; if FALSE, runs MCMC and/or bootstrapping to estimate parameter uncertainty.
use.median: Logical, if TRUE, the median of the resamples is used as the final parameter estimates; if FALSE, the initial fit by L-BFGS is used.
bootstraps.num: Integer, number of bootstrap resamples to perform. If 0, only MCMC is run.
converged.resamples.only: Logical, if TRUE, only resamples with a converged fit are used for statistical analysis; if FALSE, all resamples are used. Applies only to bootstraps.
max.fork: Integer, maximum number of parallel processes to use for bootstrapping.
verbose: Logical, if TRUE, prints information during the fitting process.
model.settings: List, settings for the C++ model, including buffer_factor, ctol, max_penalty_at_distance_factor, LROcutoff, LROwindow_factor, rise_threshold_factor, max_evals, rng_seed, warp_precision, and round_none. Default values are provided.
MCMC.settings: List, settings for the MCMC simulation, including MCMC.burnin, MCMC.steps, MCMC.step.size, MCMC.prior, and MCMC.neighbor.filter. Default values are provided.
plot.settings: List, settings for plots to make, including print.plots, dim.bounds, pred.type, count.type, splitting_factor, CI_style, label_size, title_size, axis_size, legend_size, count_size, count_jitter, count.alpha.ran, count.alpha.none, pred.alpha.ran, and pred.alpha.none. Default values are provided.

Value

List giving the results of the fitted model, including: model.component.list, count.data.summed, fitted.parameters, gamma.disperson, param.names, fix, treatment, grouping.variables, param.idx0, settings, sample.params, sample.params.bs, sample.params.MCMC, diagnostics.bs, diagnostics.MCMC, stats, and plots