Contains the Bayesian posterior predictions at short intervals specified as an argument to the $run method of PM_fit. Default is every 12 minutes.

## Details

Contains the results of makePost, which is a
data frame with Bayesian posterior predicted outputs for all subjects.
To provide a more traditional experience in R,
the data frame is separated by columns into fields, e.g. `id`

or `time`

. This
allows you to access them in an S3 way, e.g. `run1$post$time`

if `run1`

is a
`PM_result`

object.

However, if you wish to manipulate the entire data frame,
use the `data`

field, e.g. `trough <- run1$post$data %>% filter(time == 24)`

. If
you are unfamiliar with the `%>%`

pipe function, please type `help("%>%", "magrittr")`

into the R console and look online for instructions/tutorials in tidyverse, a
powerful approach to data manipulation upon which Pmetrics is built.

## Public fields

`id`

Subject id

`time`

Time of predictions in decimal hours

`icen`

Prediction based on mean or median of Bayesian posterior parameter distribution

`outeq`

Output equation number

`pred`

Predicted output for each outeq

`block`

Observation blocks within subjects as defined by

*EVID=4*dosing events`data`

A data frame combining all the above fields as its columns

## Methods

### Method `new()`

Create new object populated with Bayesian posterior predicted data at regular, frequent intervals

#### Usage

`PM_post$new(post)`

#### Arguments

`post`

The parsed output from makePost.

### Method `auc()`

Calculate AUC

#### Arguments

`...`

Arguments passed to makeAUC

`data`

The object to use for AUC calculation

#### Details

See makeAUC