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 = if (is.null(choice_effects)) {
     character()
 } else {
    
    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 when format = "wide".

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 with the identifier and covariate columns. The column roles are stored in the attributes format, column_decider, column_occasion, column_alternative, column_ac_covariates, column_as_covariates, delimiter, and cross_section, analogous to choice_data.

covariate_names() returns a character vector.

design_matrices() returns a list of class choice_design_matrices with one numeric design matrix per choice occasion, see the section below. The attributes Tp (the number of choice occasions per decider), alternatives, availability (the indices of the available alternatives per occasion), and choice_type describe the structure.

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>