update.RprobitB_fit: Update and re-fit probit model
In RprobitB: Bayesian Probit Choice Modeling

update.RprobitB_fit

R Documentation

Update and re-fit probit model

Description

This function estimates a nested probit model based on a given RprobitB_fit object.

Usage

## S3 method for class 'RprobitB_fit'
update(
  object,
  form,
  re,
  alternatives,
  id,
  idc,
  standardize,
  impute,
  scale,
  R,
  B,
  Q,
  print_progress,
  prior,
  latent_classes,
  seed,
  ...
)

Arguments

`object`	An object of class `RprobitB_fit`.
`form`	A `formula` object that is used to specify the model equation. The structure is `choice ~ A \| B \| C`, where `choice` is the name of the dependent variable (the choices), `A` are names of alternative and choice situation specific covariates with a coefficient that is constant across alternatives, `B` are names of choice situation specific covariates with alternative specific coefficients, and `C` are names of alternative and choice situation specific covariates with alternative specific coefficients. Multiple covariates (of one type) are separated by a `+` sign. By default, alternative specific constants (ASCs) are added to the model. They can be removed by adding `+0` in the second spot. In the ordered probit model (`ordered = TRUE`), the `formula` object has the simple structure `choice ~ A`. ASCs are not estimated.
`re`	A character (vector) of covariates of `form` with random effects. If `re = NULL` (the default), there are no random effects. To have random effects for the ASCs, include `"ASC"` in `re`.
`alternatives`	A character vector with the names of the choice alternatives. If not specified, the choice set is defined by the observed choices. If `ordered = TRUE`, `alternatives` is assumed to be specified with the alternatives ordered from worst to best.
`id`	A character, the name of the column in `choice_data` that contains unique identifier for each decision maker. The default is `"id"`.
`idc`	A character, the name of the column in `choice_data` that contains unique identifier for each choice situation of each decision maker. Per default, these identifier are generated by the order of appearance.
`standardize`	A character vector of names of covariates that get standardized. Covariates of type 1 or 3 have to be addressed by `<covariate>_<alternative>`. If `standardize = "all"`, all covariates get standardized.
`impute`	A character that specifies how to handle missing covariate entries in `choice_data`, one of: `"complete_cases"`, removes all rows containing missing covariate entries (the default), `"zero"`, replaces missing covariate entries by zero (only for numeric columns), `"mean"`, imputes missing covariate entries by the mean (only for numeric columns).
`scale`	A character which determines the utility scale. It is of the form `⁠<parameter> := <value>⁠`, where `⁠<parameter>⁠` is either the name of a fixed effect or `⁠Sigma_<j>,<j>⁠` for the `⁠<j>⁠`th diagonal element of `Sigma`, and `⁠<value>⁠` is the value of the fixed parameter.
`R`	The number of iterations of the Gibbs sampler.
`B`	The length of the burn-in period, i.e. a non-negative number of samples to be discarded.
`Q`	The thinning factor for the Gibbs samples, i.e. only every `Q`th sample is kept.
`print_progress`	A boolean, determining whether to print the Gibbs sampler progress and the estimated remaining computation time.
`prior`	A named list of parameters for the prior distributions. See the documentation of `check_prior` for details about which parameters can be specified.
`latent_classes`	Either `NULL` (for no latent classes) or a list of parameters specifying the number of latent classes and their updating scheme: `C`: The fixed number (greater or equal 1) of latent classes, which is set to 1 per default. If either `weight_update = TRUE` or `dp_update = TRUE` (i.e. if classes are updated), `C` equals the initial number of latent classes. `weight_update`: A boolean, set to `TRUE` to weight-based update the latent classes. See ... for details. `dp_update`: A boolean, set to `TRUE` to update the latent classes based on a Dirichlet process. See ... for details. `Cmax`: The maximum number of latent classes. `buffer`: The number of iterations to wait before a next weight-based update of the latent classes. `epsmin`: The threshold weight (between 0 and 1) for removing a latent class in the weight-based updating scheme. `epsmax`: The threshold weight (between 0 and 1) for splitting a latent class in the weight-based updating scheme. `distmin`: The (non-negative) threshold in class mean difference for joining two latent classes in the weight-based updating scheme.
`seed`	Set a seed for the Gibbs sampling.
`...`	Ignored.