choice_covariates: Define choice covariates

In choicedata: Working with Choice Data

Define choice covariates

Description

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 in data_frame.
delimiter	[character(1)] Delimiter separating alternative identifiers from covariate names in wide format. May consist of one or more characters.
cross_section	[logical(1)] Treat choice data as cross-sectional?
choice_effects	[choice_effects | NULL] Optional choice_effects object used to align covariate labels.
choice_identifiers	[choice_identifiers] A choice_identifiers object describing the simulated panel.
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

A tibble.

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. See compute_P to compute the number P.

Examples

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)
)

ids <- generate_choice_identifiers(N = 3, Tp = 2)

choice_covariates <- generate_choice_covariates(
  choice_effects = choice_effects,
  choice_identifiers = ids
)

choicedata documentation built on Nov. 5, 2025, 5:46 p.m.