Define choice covariates

The choice_covariates object defines the choice model covariates.

• generate_choice_covariates() samples covariates.

• covariate_names() gives the covariate names for given choice_effects.

• design_matrices() builds design matrices.

Usage

choice_covariates(
  data_frame,
  format = "wide",
  column_decider = "deciderID",
  column_occasion = NULL,
  column_alternative = NULL,
  column_ac_covariates = NULL,
  column_as_covariates = NULL,
  delimiter = "_",
  cross_section = is.null(column_occasion)
)

generate_choice_covariates(
  choice_effects = NULL,
  choice_identifiers = generate_choice_identifiers(N = 100),
  labels = covariate_names(choice_effects),
  n = nrow(choice_identifiers),
  marginals = list(),
  correlation = diag(length(labels)),
  verbose = FALSE,
  delimiter = "_"
)

covariate_names(choice_effects)

design_matrices(
  x,
  choice_effects,
  choice_identifiers = extract_choice_identifiers(x)
)

Arguments

data_frame

[data.frame]
Contains the choice covariates.

format

[character(1)]
Format of data_frame. Use "wide" when covariates for all alternatives are stored in a single row per occasion and "long" when each alternative forms a separate row.

column_decider

[character(1)]
Column name with decider identifiers.

column_occasion

[character(1) | NULL]
Column name with occasion identifiers. Set to NULL for cross-sectional data.

column_alternative

[character(1) | NULL]
Column name with alternative identifiers when format = "long".

column_ac_covariates

[character() | NULL]
Column names with alternative-constant covariates.

column_as_covariates

[character() | NULL]
Column names with alternative-specific covariates.

delimiter

[character(1)]
Delimiter separating alternative identifiers from covariate names in wide format.

cross_section

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

choice_effects

[choice_effects | NULL]
A choice_effects object.

choice_identifiers

[choice_identifiers]
A choice_identifiers object.

labels

[character()]
Unique labels for the regressors.

n

[integer(1)]
The number of values per regressor.

marginals

[list()]
Optionally marginal distributions for regressors. If not specified, standard normal marginal distributions are used.

Each list entry must be named according to a regressor label, and the following distributions are currently supported:

discrete distributions

• Poisson: list(type = "poisson", lambda = ...)

• categorical: list(type = "categorical", p = c(...))

continuous distributions

• normal: list(type = "normal", mean = ..., sd = ...)

• uniform: list(type = "uniform", min = ..., max = ...)

correlation

[matrix()]
A correlation matrix of dimension length(labels), where the (p, q)-th entry defines the correlation between regressor labels[p] and labels[q].

verbose

[logical(1)]
Print information about the simulated regressors?

x

A choice_data or choice_covariates object.

Value

choice_covariates() and generate_choice_covariates() return a choice_covariates tibble. covariate_names() returns a character vector. design_matrices() returns one numeric design matrix per choice occasion in a list; its Tp attribute records the panel lengths.

Design matrices

A covariate design matrix contains the choice covariates of a decider at a choice occasion. It is of dimension J x P, where J is the number of choice alternatives and P the number of effects.

Examples

### sample covariates from choice effects
choice_effects <- choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ price | income | comfort,
    error_term = "probit",
    random_effects = c(
      "price" = "cn",
      "income" = "cn"
    )
  ),
  choice_alternatives = choice_alternatives(J = 3)
)
(choice_covariates <- generate_choice_covariates(
  choice_effects = choice_effects,
  choice_identifiers = generate_choice_identifiers(N = 3, Tp = 2)
))
#> # A tibble: 6 × 9
#>   deciderID occasionID  income price_A price_B price_C comfort_A comfort_B
#> * <chr>     <chr>        <dbl>   <dbl>   <dbl>   <dbl>     <dbl>     <dbl>
#> 1 1         1          -1.82    -1.40    0.255  -2.44   -0.00557    0.622 
#> 2 1         2          -1.63    -0.247  -0.244  -0.283  -0.554      0.629 
#> 3 2         1           0.468    0.512  -1.86   -0.522  -0.0526     0.543 
#> 4 2         2          -0.0160   0.363  -1.30    0.738   1.89      -0.0974
#> 5 3         1           0.112   -0.827  -1.51    0.935   0.176      0.244 
#> 6 3         2          -0.639   -0.134  -1.91   -0.279  -0.313      1.07  
#> # ℹ 1 more variable: comfort_C <dbl>