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 adjusted predictions at these representative values.

## Usage

``````values_at(x, values = "meansd")

representative_values(x, values = "meansd")``````

## 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.

• an option to compute a range of percentiles is also possible, using `"percentile"`, followed by the percentage of the range. For example, `"percentile95"` will calculate the 95% range of the variable.

• `"all"`: uses all values of the moderator variable.

## 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
``````