Update an mlmodel Call

Usage

# S3 method for class 'mlmodel'
update(
  object,
  formula. = NULL,
  scale. = NULL,
  data = NULL,
  weights = NULL,
  subset = NULL,
  ...,
  evaluate = TRUE
)

# S3 method for class 'ml_poisson'
update(
  object,
  formula. = NULL,
  data = NULL,
  weights = NULL,
  subset = NULL,
  ...,
  evaluate = TRUE
)

Arguments

object: An mlmodel object.
formula.: An updated formula for the value (location/mean) equation.
scale.: An updated formula for the scale equation (if supported by the model).
data: A data frame to be used when re-fitting the model.
weights: Optional case weights.
subset: An expression or logical vector to subset, or a vector of indices to resample (sandwich uses it for that).
...: Further arguments passed to methods (currently ignored).
evaluate: Logical. If TRUE (default), the updated call is evaluated and the new fitted model is returned. If FALSE, the updated call (as a language object) is returned without evaluation.

Details

This method re-evaluates the original model call after modifying selected arguments. It is used internally by IMtest() and serves as a fallback mechanism in bootstrap and jackknife variance estimation when a model-specific implementation is not available.

sandwich package compatibility

The functions sandwich::vcovBS() and sandwich::vcovJK() are supported through this update() method. They produce numerically equivalent results to our own vcov(object, type = "boot") and vcov(object, type = "jack") when all bootstrap/jackknife replications converge, taking longer to compute them.

Important difference: When some replications fail to converge, sandwich includes those failed iterations in the variance calculation, while our vcov() implementation uses only successful replications. The latter is statistically more appropriate.

We therefore strongly recommend using the native vcov() methods provided by mlmodels for bootstrap and jackknife variance-covariance matrices.