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Downhill search among hierarchical models

downhill_fit.Rd

Starting from the main-effects model, repeatedly moves to the neighbouring hierarchical model with the smallest BIC until no improvement is available or the iteration limit is reached.

Usage

downhill_fit(
  counts,
  desmat,
  maxorder = dim(desmat)[2] - 1,
  checkid = TRUE,
  niter = 20,
  verbose = FALSE
)

Arguments

counts

Numeric vector of observed counts.

desmat

Binary matrix defining the corresponding capture histories.

maxorder

Maximum interaction order considered.

checkid

If TRUE, check parameter identifiability and existence of the extended MLE before fitting each model.

niter

Maximum number of downhill iterations.

verbose

If FALSE, return only the selected population estimate. If TRUE, return detailed search results.

Value

If verbose = FALSE, the population estimate from the selected model, or NA if the main-effects model has no valid fit. If verbose = TRUE, a list with components:

optimum_hierarchy

Selected hierarchical model.

minimum_value

Named vector containing its BIC and population estimate.

hierarchies_considered

Character vector of models examined.

function_values

Matrix containing the BIC and population estimate for each model examined.

References

Silverman, B. W., Chan, L. and Vincent, K., (2024). Bootstrapping Multiple Systems Estimates to Account for Model Selection Statistics and Computing, 34, 44. doi:10.1007/s11222-023-10346-9 .