ValidationExplorer (version 0.1.1)

run_sims: run_sims: conduct simulations easily

Description

run_sims: conduct simulations easily

Usage

run_sims(
  data_list,
  zeros_list,
  DGVs,
  theta_scenario_id,
  parallel = TRUE,
  niter = 2000,
  nburn = floor(niter/2),
  thin = 1,
  nchains = 3,
  save_fits = FALSE,
  save_individual_summaries_list = FALSE,
  directory = tempdir()
)

Value

a dataframe with the summaries (from mcmc_sum) for all scenarios and datasets. A copy of this output is also saved to the current working directory.

Arguments

data_list

nested list of masked dataframes (datasets nested within scenarios -- this is the format of masked_dfs output from simulate_validatedData)

zeros_list

list of dataframes containing the site/visit/true_spp/id_spp combinations where no calls were observed.

DGVs

A named list with entries psi, lambda and theta containing the true values of the respective parameters.

theta_scenario_id

A character string ID for the simulations being run.

parallel

Should models be fit in parallel? Default value is TRUE.

niter

Number of iterations per MCMC chain.

nburn

Number of warmup iterations.

thin

Thinning interval for the MCMC chains.

nchains

The number of chains.

save_fits

Should individual model fits be saved? This could require large amounts of disk space if you are fitting many large models to big datasets. Default value is FALSE.

save_individual_summaries_list

Should summaries for individual model fits be saved? While this requires much less space than save_fits, we still recommend keeping this at the default value of FALSE. Only use it if you anticipate that simulations may be interrupted.

directory

The directory to save objects. Defaults to tempdir(), but users should specify a permanent location for real simulation studies as tempdir() is cleared at the end of the R session.

Examples

# :::::::::::: Simulate data ::::::::::::: #
psi <- c(0.3, 0.6)
lambda <- c(11, 2)

nspecies <- length(psi)
nsites <- 30
nvisits <- 5

test_theta1 <- matrix(c(0.9, 0.1, 0.15, 0.85), byrow = TRUE, nrow = 2)
val_scenarios <- list(spp1 = c(.75, .5), spp2 = .5)

td <- withr::local_tempdir()

fake_data <- simulate_validatedData(
  n_datasets = 5,
  design_type = "BySpecies",
  scenarios = val_scenarios,
  nsites = nsites,
  nvisits = nvisits,
  nspecies = nspecies,
  psi = psi,
  lambda = lambda,
  theta = test_theta1,
  save_datasets = FALSE,
  save_masked_datasets = FALSE,
  directory = td
)

# ::::::::::::: run simulations on sim'd data ::::::::::: #

# \donttest{

out <- run_sims(
  data_list = fake_data$masked_dfs,
  zeros_list = fake_data$zeros,
  DGVs = list(lambda = lambda, psi = psi, theta = test_theta1),
  theta_scenario_id = 'StratBySpecies_1',
  parallel = FALSE,
  nchains = 2,
  niter = 500,
  nburn = 250,
  thin = 1,
  save_fits = FALSE,
  save_individual_summaries_list = FALSE,
  directory = td
)
# }