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
A

`formula`

object that is used to specify the model equation. The structure is`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
A character (vector) of covariates of

`form`

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
A character vector with 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
A character, 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). Per default,`base`

is the last element of`alternatives`

.- ordered
A boolean,

`FALSE`

per default. 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
```