input checking for make_flocker_data

standard_mfd_checks(
  obs,
  unit_covs,
  event_covs,
  type,
  n_aug,
  quiet,
  newdata_checks
)

Arguments

obs: If type = "single", an I x J matrix-like object where closure is assumed across rows and columns are repeated sampling events. If type = "multi", an I x J x K array where rows are sites or species-sites, columns are repeated sampling events, and slices along the third dimension are seasons. Allowable values are 1 (detection), 0 (no detection), and NA (no sampling event). If type = "augmented", an L x J x K array where rows L are sites, columns J are repeat sampling events, and slices K are species. The data must be packed so that, for a given unit (site, site-species, site-timestep, site-species-timestep) all realized visits come before any missing visits (NAs are trailing within their rows).
unit_covs: If type = "single" a dataframe of covariates for each closure-unit that are constant across repeated sampling events within units. If type = "multi", a list of such dataframes, one per timestep. All dataframes must have identical column names and types, and all dataframes must have I rows. If type = "augmented", a dataframe of covariates for each site that are constant across repeated sampling events within sites (no dependence on species is allowed).
event_covs: If type = "single", a named list of I x J matrices, each one corresponding to a covariate that varies across repeated sampling events within closure-units. If type = "multi", a named list of I x J x K arrays, each one corresponding to a covariate that varies across repeated sampling events within closure-units. If type = "augmented", a named list of L x J matrices, each one corresponding to a covariate that varies across repeated sampling events within sites (no dependence on species is allowed).
type: The type of occupancy model desired. Options are: "single" for a single_season model, "multi" for a multi-season (dynamic) model, or "augmented" for a single-season multi-species model with data-augmentation for never-observed pseudospecies.
n_aug: Number of pseudo-species to augment. Only applicable if type = "augmented".
quiet: Hide progress bars and informational messages?
newdata_checks: If TRUE, turn off checks that must pass in order to use the data for model fitting, but not in other contexts (e.g. making predictions or assessing log-likelihoods over new data).