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Define choice model effects

Source: R/choice_effects.R
choice_effects.Rd

This function constructs an object of class choice_effects, which defines the effects of a choice model.

Usage

choice_effects(
  choice_formula,
  choice_alternatives,
  choice_data = NULL,
  delimiter = "_"
)

# S3 method for class 'choice_effects'
print(x, ...)

Arguments

choice_formula

[choice_formula]
A choice_formula object.

choice_alternatives

[choice_alternatives]
A choice_alternatives object.

choice_data

[NULL | choice_data]
A choice_data object.

Required to resolve data-dependent elements in choice_formula (if any).

delimiter

[character(1)]
The delimiter between covariate and alternative name.

x

[choice_effects]
The choice_effects object to be printed.

...

Currently not used.

Value

A choice_effects object, which is a data.frame, where each row is a model effect, and columns are

  1. "effect_name", the name for the effect which is composed of covariate and alternative name,

  2. "generic_name", the generic effect name "beta_<effect number>",

  3. "covariate", the (transformed) covariate name connected to the effect,

  4. "alternative", the alternative name connected to the effect (only if the effect is alternative-specific),

  5. "as_covariate", indicator whether the covariate is alternative-specific,

  6. "as_effect", indicator whether the effect is alternative-specific,

  7. "mixing", a factor with levels in the order

    1. "cn" (correlated normal distribution),

    indicating the type of random effect.

For identification, the choice effects are ordered according to the following rules:

  1. Non-random effects come before random effects.

  2. According to the ordering of the factor mixing.

  3. Otherwise, the order is determined by occurrence in formula.

It contains the arguments choice_formula, choice_alternatives, and delimiter as attributes.

Examples

choice_effects(
  choice_formula = choice_formula(
    formula = choice ~ price | income | I(comfort == 1),
    error_term = "probit",
    random_effects = c(
      "price" = "cn",
      "income" = "cn"
     )
  ),
  choice_alternatives = choice_alternatives(J = 3)
)
#> 
#> ── Choice effects 
#>       effect_name generic_name     covariate alternative as_covariate as_effect
#> 1           ASC_B       beta_1          <NA>           B        FALSE      TRUE
#> 2           ASC_C       beta_2          <NA>           C        FALSE      TRUE
#> 3 I(comfort==1)_A       beta_3 I(comfort==1)           A         TRUE      TRUE
#> 4 I(comfort==1)_B       beta_4 I(comfort==1)           B         TRUE      TRUE
#> 5 I(comfort==1)_C       beta_5 I(comfort==1)           C         TRUE      TRUE
#> 6           price       beta_6         price        <NA>         TRUE     FALSE
#> 7        income_B       beta_7        income           B        FALSE      TRUE
#> 8        income_C       beta_8        income           C        FALSE      TRUE
#>   mixing
#> 1   <NA>
#> 2   <NA>
#> 3   <NA>
#> 4   <NA>
#> 5   <NA>
#> 6     cn
#> 7     cn
#> 8     cn