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Print effect overview

Source: R/overview_effects.R
overview_effects.Rd

This function gives an overview of the effect names, whether the covariate is alternative-specific, whether the coefficient is alternative-specific, and whether it is a random effect.

Usage

overview_effects(
  form,
  re = NULL,
  alternatives,
  base = tail(alternatives, 1),
  ordered = FALSE
)

Arguments

form

[formula]
A model description with the structure choice ~ A | B | C, where

  • choice is the name of the dependent variable (the choices),

  • A are names of alternative and choice situation specific covariates with a coefficient that is constant across alternatives,

  • B are names of choice situation specific covariates with alternative specific coefficients,

  • and C are names of alternative and choice situation specific covariates with alternative specific coefficients.

Multiple covariates (of one type) are separated by a + sign. By default, alternative specific constants (ASCs) are added to the model. They can be removed by adding +0 in the second spot.

In the ordered probit model (ordered = TRUE), the formula object has the simple structure choice ~ A. ASCs are not estimated.

re

[character() | NULL]
Names of covariates with random effects. If re = NULL (the default), there are no random effects. To have random effects for the ASCs, include "ASC" in re.

alternatives

[character()]
The names of the choice alternatives. If not specified, the choice set is defined by the observed choices.

If ordered = TRUE, alternatives is assumed to be specified with the alternatives ordered from worst to best.

base

[character(1)]
The name of the base alternative for covariates that are not alternative specific (i.e. type 2 covariates and ASCs).

Ignored and set to NULL if the model has no alternative specific covariates (e.g. in the ordered probit model).

By default, base is the last element of alternatives.

ordered

[logical(1)]
If TRUE, the choice set alternatives is assumed to be ordered from worst to best.

Value

A data.frame, each row is a effect, columns are the effect name "effect", and booleans whether the covariate is alternative-specific "as_value", whether the coefficient is alternative-specific "as_coef", and whether it is a random effect "random".

See also

check_form() for checking the model formula specification.

Examples

overview_effects(
  form = choice ~ price + time + comfort + change | 1,
  re = c("price", "time"),
  alternatives = c("A", "B"),
  base = "A"
)
#>    effect as_value as_coef random
#> 1 comfort     TRUE   FALSE  FALSE
#> 2  change     TRUE   FALSE  FALSE
#> 3   ASC_B    FALSE    TRUE  FALSE
#> 4   price     TRUE   FALSE   TRUE
#> 5    time     TRUE   FALSE   TRUE