update.RprobitB_fit {RprobitB}

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 Qth 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.

Details

All parameters (except for object) are optional and if not specified retrieved from the specification for object.

Value

An object of class RprobitB_fit.