## Details

Contains the results of makeOP, which is a
data frame suitable for analysis and plotting of observed vs. population or
or individual predicted outputs. 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$op$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$op$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 identification

`time`

observation time in relative units, usually hours

`obs`

observation

`pred`

prediction

`pred.type`

Population predictions based on Bayesian prior parameter value distribution, or individual predictions based on Bayesian posterior parameter value distributions

`icen`

Predictions based on mean or median of Bayesian

`pred.type`

parameter values`outeq`

output equation number

`block`

dosing block number for each subject, as defined by dose resets (evid=4).

`obsSD`

standard deviation of the observation based on the assay error polynomial

`d`

prediction error,

`pred`

-`obs`

`ds`

squared prediction error

`wd`

weighted prediction error, which is the prediction error divided by the

`obsSD`

`wds`

weighted squared prediction error

`data`

A data frame combining all the above fields as its columns

## Methods

### Method `new()`

Create new object populated with observed vs. predicted data

#### Usage

`PM_op$new(op)`

#### Arguments

`op`

The parsed output from makeOP.

### Method `summary()`

Summary method

#### Arguments

`...`

Arguments passed to summary.PMop

#### Details

See summary.PMop.

### Method `auc()`

Calculate AUC

#### Arguments

`...`

Arguments passed to makeAUC

`data`

The object to use for AUC calculation

#### Details

See makeAUC