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Define choice covariates

Source: R/choice_covariates.R
choice_covariates.Rd

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
)