Fit probit model to choice data

This function performs Markov chain Monte Carlo simulation for fitting different types of probit models (binary, multivariate, mixed, latent class, ordered, ranked) to discrete choice data.

Usage

fit_model(
  data,
  scale = "Sigma_1,1 := 1",
  R = 1000,
  B = R/2,
  Q = 1,
  print_progress = getOption("RprobitB_progress"),
  prior = NULL,
  latent_classes = NULL,
  seed = NULL,
  fixed_parameter = list()
)

Arguments

data

An object of class RprobitB_data.

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.

fixed_parameter

Optionally specify a named list with fixed parameter values for alpha, C, s, b, Omega, Sigma, Sigma_full, beta, z, or d for the simulation. See the vignette on model definition for definitions of these variables.

Value

An object of class RprobitB_fit.

Details

See the vignette on model fitting for more details.

Examples

data <- simulate_choices(
  form = choice ~ var | 0, N = 100, T = 10, J = 3, seed = 1
)
model <- fit_model(data = data, R = 1000, seed = 1)
#> Computing sufficient statistics - 0 of 4  

#> Computing sufficient statistics - 1 of 4  

#> Computing sufficient statistics - 2 of 4  

#> Computing sufficient statistics - 3 of 4  

#> Computing sufficient statistics - 4 of 4  

#> MCMC iteration - 1 of 1000  

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#> Computing log-likelihood
summary(model)
#> Probit model
#> Formula: choice ~ var | 0 
#> R: 1000, B: 500, Q: 1
#> Level: Utility differences with respect to alternative 'C'.
#> Scale: Coefficient of the 1. error term variance fixed to 1.
#> 
#> Gibbs sample statistics
#>           true    mean      sd      R^
#>  alpha
#>                                       
#>      1   -0.94   -0.85    0.06    1.16
#> 
#>  Sigma
#>                                       
#>    1,1    1.00    1.00    0.00    1.00
#>    1,2   -0.42   -0.30    0.06    2.15
#>    2,2    0.27    0.19    0.04    1.62

Usage

Arguments

Value

Details

See also

Examples