Fit probit model to choice data

This function performs MCMC 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", default = TRUE),
  prior = NULL,
  latent_classes = NULL,
  fixed_parameter = list(),
  save_beta_draws = FALSE
)

Arguments

data

An object of class RprobitB_data.

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.

fixed_parameter

[list]
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.

save_beta_draws

[logical(1)]
Save draws for decider-specific coefficient vectors? Usually not recommended, as it requires a lot of storage space.

Value

An object of class RprobitB_fit.

Examples

set.seed(1)
form <- choice ~ var | 0
data <- simulate_choices(form = form, N = 100, T = 10, J = 3, re = "var")
model <- fit_model(data = data, R = 1000)
#> 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  

#> Gibbs sampler - 1 of 1000 iterations 

#> Gibbs sampler - 10 of 1000 iterations 

#> Gibbs sampler - 20 of 1000 iterations 

#> Gibbs sampler - 30 of 1000 iterations 

#> Gibbs sampler - 40 of 1000 iterations 

#> Gibbs sampler - 50 of 1000 iterations 

#> Gibbs sampler - 60 of 1000 iterations 

#> Gibbs sampler - 70 of 1000 iterations 

#> Gibbs sampler - 80 of 1000 iterations 

#> Gibbs sampler - 90 of 1000 iterations 

#> Gibbs sampler - 100 of 1000 iterations 

#> Gibbs sampler - 110 of 1000 iterations 

#> Gibbs sampler - 120 of 1000 iterations 

#> Gibbs sampler - 130 of 1000 iterations 

#> Gibbs sampler - 140 of 1000 iterations 

#> Gibbs sampler - 150 of 1000 iterations 

#> Gibbs sampler - 160 of 1000 iterations 

#> Gibbs sampler - 170 of 1000 iterations 

#> Gibbs sampler - 180 of 1000 iterations 

#> Gibbs sampler - 190 of 1000 iterations 

#> Gibbs sampler - 200 of 1000 iterations 

#> Gibbs sampler - 210 of 1000 iterations 

#> Gibbs sampler - 220 of 1000 iterations 

#> Gibbs sampler - 230 of 1000 iterations 

#> Gibbs sampler - 240 of 1000 iterations 

#> Gibbs sampler - 250 of 1000 iterations 

#> Gibbs sampler - 260 of 1000 iterations 

#> Gibbs sampler - 270 of 1000 iterations 

#> Gibbs sampler - 280 of 1000 iterations 

#> Gibbs sampler - 290 of 1000 iterations 

#> Gibbs sampler - 300 of 1000 iterations 

#> Gibbs sampler - 310 of 1000 iterations 

#> Gibbs sampler - 320 of 1000 iterations 

#> Gibbs sampler - 330 of 1000 iterations 

#> Gibbs sampler - 340 of 1000 iterations 

#> Gibbs sampler - 350 of 1000 iterations 

#> Gibbs sampler - 360 of 1000 iterations 

#> Gibbs sampler - 370 of 1000 iterations 

#> Gibbs sampler - 380 of 1000 iterations 

#> Gibbs sampler - 390 of 1000 iterations 

#> Gibbs sampler - 400 of 1000 iterations 

#> Gibbs sampler - 410 of 1000 iterations 

#> Gibbs sampler - 420 of 1000 iterations 

#> Gibbs sampler - 430 of 1000 iterations 

#> Gibbs sampler - 440 of 1000 iterations 

#> Gibbs sampler - 450 of 1000 iterations 

#> Gibbs sampler - 460 of 1000 iterations 

#> Gibbs sampler - 470 of 1000 iterations 

#> Gibbs sampler - 480 of 1000 iterations 

#> Gibbs sampler - 490 of 1000 iterations 

#> Gibbs sampler - 500 of 1000 iterations 

#> Gibbs sampler - 510 of 1000 iterations 

#> Gibbs sampler - 520 of 1000 iterations 

#> Gibbs sampler - 530 of 1000 iterations 

#> Gibbs sampler - 540 of 1000 iterations 

#> Gibbs sampler - 550 of 1000 iterations 

#> Gibbs sampler - 560 of 1000 iterations 

#> Gibbs sampler - 570 of 1000 iterations 

#> Gibbs sampler - 580 of 1000 iterations 

#> Gibbs sampler - 590 of 1000 iterations 

#> Gibbs sampler - 600 of 1000 iterations 

#> Gibbs sampler - 610 of 1000 iterations 

#> Gibbs sampler - 620 of 1000 iterations 

#> Gibbs sampler - 630 of 1000 iterations 

#> Gibbs sampler - 640 of 1000 iterations 

#> Gibbs sampler - 650 of 1000 iterations 

#> Gibbs sampler - 660 of 1000 iterations 

#> Gibbs sampler - 670 of 1000 iterations 

#> Gibbs sampler - 680 of 1000 iterations 

#> Gibbs sampler - 690 of 1000 iterations 

#> Gibbs sampler - 700 of 1000 iterations 

#> Gibbs sampler - 710 of 1000 iterations 

#> Gibbs sampler - 720 of 1000 iterations 

#> Gibbs sampler - 730 of 1000 iterations 

#> Gibbs sampler - 740 of 1000 iterations 

#> Gibbs sampler - 750 of 1000 iterations 

#> Gibbs sampler - 760 of 1000 iterations 

#> Gibbs sampler - 770 of 1000 iterations 

#> Gibbs sampler - 780 of 1000 iterations 

#> Gibbs sampler - 790 of 1000 iterations 

#> Gibbs sampler - 800 of 1000 iterations 

#> Gibbs sampler - 810 of 1000 iterations 

#> Gibbs sampler - 820 of 1000 iterations 

#> Gibbs sampler - 830 of 1000 iterations 

#> Gibbs sampler - 840 of 1000 iterations 

#> Gibbs sampler - 850 of 1000 iterations 

#> Gibbs sampler - 860 of 1000 iterations 

#> Gibbs sampler - 870 of 1000 iterations 

#> Gibbs sampler - 880 of 1000 iterations 

#> Gibbs sampler - 890 of 1000 iterations 

#> Gibbs sampler - 900 of 1000 iterations 

#> Gibbs sampler - 910 of 1000 iterations 

#> Gibbs sampler - 920 of 1000 iterations 

#> Gibbs sampler - 930 of 1000 iterations 

#> Gibbs sampler - 940 of 1000 iterations 

#> Gibbs sampler - 950 of 1000 iterations 

#> Gibbs sampler - 960 of 1000 iterations 

#> Gibbs sampler - 970 of 1000 iterations 

#> Gibbs sampler - 980 of 1000 iterations 

#> Gibbs sampler - 990 of 1000 iterations 

#> Gibbs sampler - 1000 of 1000 iterations 

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.
#> 
#> Latent classes
#> C = 1 
#> 
#> Gibbs sample statistics
#>           true    mean      sd      R^
#>  b
#>                                       
#>    1.1    0.47    0.62    0.09    1.01
#> 
#>  Omega
#>                                       
#>  1.1,1    0.35    0.52    0.15    1.01
#> 
#>  Sigma
#>                                       
#>    1,1    1.00    1.00    0.00    1.00
#>    1,2    1.30    1.20    0.13    1.01
#>    2,2    2.79    2.86    0.55    1.04

Usage

Arguments

Value

See also

Examples