## Usage

```
makeAUC(
data,
formula,
include,
exclude,
start = 0,
end = Inf,
icen = "median",
outeq = 1,
block = 1,
method = "linear",
addZero = F
)
```

## Arguments

- data
A suitable data object, i.e. PM_pop, PM_post, PM_op, PM_sim, or some other suitable dataframe with at least time/observation columns referred to by

`formula`

, with an "id" column (so named) if necessary.- formula
A formula of the form

`obs ~ time`

. This is only required with data that is not of class PM_pop, PM_post, PM_op or PM_sim.- include
A vector of subject IDs to include in the plot, e.g. c(1:3,5,15)

- exclude
A vector of subject IDs to exclude in the plot, e.g. c(4,6:14,16:20)

- start
Specify the time to begin AUC calculations. Default is 0.

- end
Specify the time to end AUC calculations so that AUC is calculated from

`start`

to`end`

. Default for end is the maximum observation time for each subject. Subjects with insufficient data for a specified interval will have AUC calculated for the available data, not to exceed the specified interval.- icen
Can be either "median" for the predictions based on medians of

`pred.type`

parameter value distributions, or "mean". Default is "median". Only relevant for PMpost or PMpop objects.- outeq
Which output equation to plot. Default is 1.

- block
Which block to plot, where blocks are defined by dose reset events (EVID = 4) in the data.

- method
Default is "linear" for AUC trapezoidal calculation. Any other value will result in linear up, log down.

- addZero
Boolean to add a zero concentration at time 0. Default is

`FALSE`

.

## Value

A dataframe of class *PMauc*, which has 2 columns:

id - Subject identification

tau - AUC from

`start`

to`end`

## Details

Calculates the area under the time concentration curve using the
trapezoidal approximation from a variety of inputs.
If a PM_pop, PM_post, PM_op, or PMsim object is specified,
`formula`

is not required.

## Examples

```
NPex$op$auc()
#> # A tibble: 20 × 2
#> # Groups: id [20]
#> id tau
#> <dbl> <dbl>
#> 1 1 448.
#> 2 2 154.
#> 3 3 147.
#> 4 4 126.
#> 5 5 89.9
#> 6 6 312.
#> 7 7 260.
#> 8 8 310.
#> 9 9 208.
#> 10 10 237.
#> 11 11 186.
#> 12 12 234.
#> 13 13 294.
#> 14 14 152.
#> 15 15 224.
#> 16 16 246.
#> 17 17 206.
#> 18 18 288.
#> 19 19 471.
#> 20 20 286.
Indometh %>% dplyr::mutate(id = Subject) %>% makeAUC(conc ~ time)
#> # A tibble: 6 × 2
#> # Groups: id [6]
#> id tau
#> <ord> <dbl>
#> 1 1 1.55
#> 2 2 2.68
#> 3 3 2.59
#> 4 4 2.25
#> 5 5 1.70
#> 6 6 2.58
```