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Update and re-fit probit model

Source: R/update.RprobitB_fit.R
update.RprobitB_fit.Rd

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,
  ...
)

Arguments

object

An object of class RprobitB_fit.

form

[formula]
A model description with the structure 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

[character() | NULL]
Names of covariates 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

[character()]
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

[character(1)]
The name of the column in choice_data that contains unique identifier for each decision maker.

idc

[character(1)]
The name of the column in choice_data that contains unique identifier for each choice situation of each decision maker. By default, these identifier are generated by the order of appearance.

standardize

[character() | "all"]
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

[character(1)]
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

[integer(1)]
The number of iterations of the Gibbs sampler.

B

[integer(1)]
The length of the burn-in period.

Q

[integer(1)]
The thinning factor for the Gibbs samples.

print_progress

[logical(1)]
Print the Gibbs sampler progress?

prior

[list]
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

[list() | NULL]
Optionally parameters specifying the number of latent classes and their updating scheme. The values in brackets are the default.

  • C (1): The fixed number (greater or equal 1) of (initial) classes.

  • wb_update (FALSE): Set to TRUE for weight-based class updates.

  • dp_update (FALSE): Set to TRUE for Dirichlet process class updates.

  • Cmax (10): The maximum number of latent classes.

The following specifications are used for the weight-based updating scheme:

  • buffer (50): The number of iterations to wait before the next update.

  • epsmin (0.01): The threshold weight for removing a latent class.

  • epsmax (0.7): The threshold weight for splitting a latent class.

  • deltamin (0.1): The minimum mean distance before merging two classes.

  • deltashift (0.5): The scale for shifting the class means after a split.

...

Currently not used.

Value

An object of class RprobitB_fit.

Details

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