Define choice probabilities — choice_probabilities • choicedata

The choice_probabilities object defines the choice probabilities.

compute_choice_probabilities() calculates the choice probabilities based on the choice parameters and the choice data.

Usage

choice_probabilities(
  data_frame,
  choice_only = TRUE,
  column_decider = "deciderID",
  column_occasion = NULL,
  cross_section = is.null(column_occasion),
  column_probabilities = NULL,
  logarithm = FALSE,
  aggregate = c("occasion", "decider")
)

compute_choice_probabilities(
  choice_parameters,
  choice_data,
  choice_effects,
  choice_only = TRUE,
  input_checks = TRUE,
  aggregate = c("occasion", "decider"),
  logarithm = FALSE,
  ...
)

Arguments

data_frame

[data.frame]
Contains the choice probabilities.

choice_only

[logical(1)]
Only the probabilities for the chosen alternatives?

column_decider

[character(1)]
The name of the identifier column for deciders.

column_occasion

[character(1) | NULL]
The name of the identifier column for choice occasions (panel data). Can be NULL for the cross-sectional case.

cross_section

[logical(1)]
Treat choice data as cross-sectional?

column_probabilities

[character()]
The column name of the data_frame with the choice probabilities for all choice alternatives.

If choice_only = TRUE, it is the name of a single column that contains the probabilities for the chosen alternatives.

logarithm

[logical(1)]
Are the supplied or requested values log-probabilities?

aggregate

[character(1)]
Probability unit. "occasion" returns one result per choice occasion. "decider" returns the joint result for each decider's observed sequence of choices.

choice_parameters

[choice_parameters | numeric() | list()]
A choice_parameters object. A numeric vector in optimization space, as created by switch_parameter_space, is also accepted.

choice_data

[choice_data]
A choice_data object.

choice_effects

[choice_effects]
A choice_effects object.

input_checks

[logical(1)]
Should additional internal input checks be performed before computing the probabilities?

...

Additional arguments for the probability computation:

n_draws [integer(1)]: The number of draws for simulated probabilities, which are required for mixed logit models and for mixed probit models with non-normal random effects. The default is 200.
draws [matrix]: A matrix of standard normal draws with one column per random effect that replaces the generated draws.
cml [character(1)]: Composite marginal likelihood for panel probit models. Either "no" (default, the full likelihood), "fp" (all pairs of choice occasions), or "ap" (adjacent pairs of choice occasions).
ghk_draws [integer(1)]: The number of draws of the GHK simulator for multivariate normal probabilities in probit models, see pmvnorm. Probabilities of up to three dimensions are computed exactly; higher dimensions use the GHK simulator on a fixed sequence of quasi-random Halton points. The default is 500.

Value

A choice_probabilities tibble with the identifier columns followed by the probability column(s). If choice_only = TRUE, there is a single column choice_probability (or log_choice_probability if logarithm = TRUE). Otherwise, there is one column per choice alternative. The attributes column_decider, column_occasion, cross_section, column_probabilities, choice_only, logarithm, and aggregate store the column roles and the probability type.

If choice_only = FALSE and aggregate = "decider", the tibble instead has one row per possible outcome sequence and decider, with the sequence in the list column outcome.

Examples

### multinomial logit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "logit"
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(beta = c(0.2, -0.1))
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                       0.520

### multinomial probit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "probit"
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(
  beta = c(0.2, -0.1),
  Sigma = diag(c(0, 1))
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                       0.468

### ordered logit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "logit"
  ),
  choice_alternatives = choice_alternatives(
    J = 3,
    ordered = TRUE
  )
)
parameters <- choice_parameters(
  beta = c(0.2, -0.1),
  gamma = c(0, 1)
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters,
  choice_type = "ordered"
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                       0.213

### ordered probit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "probit"
  ),
  choice_alternatives = choice_alternatives(
    J = 3,
    ordered = TRUE
  )
)
parameters <- choice_parameters(
  beta = c(0.2, -0.1),
  Sigma = 1,
  gamma = c(0, 1)
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters,
  choice_type = "ordered"
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                       0.288

### ranked logit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "logit"
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(beta = c(0.2, -0.1))
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters,
  choice_type = "ranked"
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                       0.520

### ranked probit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "probit"
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(
  beta = c(0.2, -0.1),
  Sigma = diag(c(0, 1))
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters,
  choice_type = "ranked"
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                       0.468

### mixed logit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "logit",
    random_effects = c(
      x = "cn",
      z = "cn"
    )
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(
  beta = c(0.2, -0.1),
  Omega = matrix(c(0.1, 0.02, 0.02, 0.1), nrow = 2)
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                       0.508

### mixed probit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "probit",
    random_effects = c(
      x = "cn",
      z = "cn"
    )
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(
  beta = c(0.2, -0.1),
  Omega = matrix(c(0.1, 0.02, 0.02, 0.1), nrow = 2),
  Sigma = diag(c(0, 1))
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                       0.525

### panel logit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "logit",
    random_effects = c(
      x = "cn",
      z = "cn"
    )
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(
  beta = c(0.2, -0.1),
  Omega = matrix(c(0.1, 0.02, 0.02, 0.1), nrow = 2)
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(
    N = 1L,
    Tp = 2L
  ),
  choice_parameters = parameters
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 2 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                       0.509
#> 2 1         2                       0.565

### panel probit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "probit",
    random_effects = c(
      x = "cn",
      z = "cn"
    )
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(
  beta = c(0.2, -0.1),
  Omega = matrix(c(0.1, 0.02, 0.02, 0.1), nrow = 2),
  Sigma = diag(c(0, 1))
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(
    N = 1L,
    Tp = 2L
  ),
  choice_parameters = parameters
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects,
  aggregate = "decider",
  cml = "ap"
)
#> # A tibble: 1 × 2
#>   deciderID choice_probability
#> * <chr>                  <dbl>
#> 1 1                      0.292

### latent class logit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "logit"
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(
  beta = list(c(0.2, -0.1), c(-0.2, 0.1)),
  weights = c(0.5, 0.5)
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                         0.5

### latent class probit
set.seed(1)
effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ x + z | 0,
    error_term = "probit"
  ),
  choice_alternatives = choice_alternatives(J = 2)
)
parameters <- choice_parameters(
  beta = list(c(0.2, -0.1), c(-0.2, 0.1)),
  Sigma = diag(c(0, 1)),
  weights = c(0.5, 0.5)
)
simulated_data <- generate_choice_data(
  choice_effects = effects,
  choice_identifiers = generate_choice_identifiers(N = 1L),
  choice_parameters = parameters
)
compute_choice_probabilities(
  choice_parameters = parameters,
  choice_data = simulated_data,
  choice_effects = effects
)
#> # A tibble: 1 × 3
#>   deciderID occasionID choice_probability
#> * <chr>     <chr>                   <dbl>
#> 1 1         1                         0.5