This function calculates representative values of a vector, like minimum/maximum values or lower, median and upper quartile etc., which can be used for numeric vectors to plot marginal effects at these representative values.

## Arguments

- x
A numeric vector.

- values
Character vector, naming a pattern for which representative values should be calculcated.

`"minmax":`

(default) minimum and maximum values (lower and upper bounds) of the moderator are used to plot the interaction between independent variable and moderator.`"meansd"`

: uses the mean value of the moderator as well as one standard deviation below and above mean value to plot the effect of the moderator on the independent variable.`"zeromax"`

: is similar to the`"minmax"`

option, however,`0`

is always used as minimum value for the moderator. This may be useful for predictors that don't have an empirical zero-value, but absence of moderation should be simulated by using 0 as minimum.`"fivenum":`

calculates and uses the Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum) of the moderator value.`"quart"`

: calculates and uses the quartiles (lower, median and upper) of the moderator value,*including*minimum and maximum value.`"quart2"`

: calculates and uses the quartiles (lower, median and upper) of the moderator value,*excluding*minimum and maximum value.`"terciles"`

: calculates and uses the terciles (lower and upper third) of the moderator value,*including*minimum and maximum value.`"terciles2"`

: calculates and uses the terciles (lower and upper third) of the moderator value,*excluding*minimum and maximum value.`"all"`

: uses all values of the moderator variable. Note that this option only applies to`type = "eff"`

, for numeric moderator values.

## Value

A numeric vector of length two or three, representing the required
values from `x`

, like minimum/maximum value or mean and +/- 1 SD. If
`x`

is missing, a function, pre-programmed with `n`

and
`length`

is returned. See examples.

## Examples

```
data(efc)
values_at(efc$c12hour)
#> [1] -8.4 42.4 93.2
values_at(efc$c12hour, "quart2")
#> [1] 10.0 20.0 42.8
mean_sd <- values_at(values = "meansd")
mean_sd(efc$c12hour)
#> [1] -8.4 42.4 93.2
```