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Observed vs. predicted data

Source: R/PM_op.R
PM_op.Rd

[Stable]

Contains observed vs. predicted data after a run, typically a field in a PM_result

Details

The PM_op object is both a data field within a PM_result, and itself an R6 object comprising data fields and associated methods suitable for analysis and plotting of observed vs. population or individual predicted outputs.

Because PM_op objects are automatically added to the PM_result at the end of a successful run, it is generally not necessary for users to generate PM_op objects themselves.

The main results are contained in the $data field, and it is this field which is passed to the $plot and $summary methods. You can use this $data field for custom manipulations, e.g. trough <- run1$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.

To provide a more traditional experience in R, the $data field is also separated by columns into the other data fields within the R6 object, e.g. id or time. This allows you to access them in an S3 way, e.g. run1$op$time if run1 is a PM_result object.

Author

Michael Neely, Julian Otalvaro

Public fields

id

subject identification

time

observation time in relative units, usually hours

obs

observation

cens

censoring information: "none" for observed, "bloq" for below limit of quantification, "aloq" for above limit of quantification

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.typeparameter 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 of class PM_op_data combining all the above fields as its columns

Methods

Public methods

Method new()

Create new object populated with observed vs. predicted data

Usage

PM_op$new(PMdata = NULL, path = ".", ...)

Arguments

PMdata

include Saved, parsed output of prior run, used when source files are not available. .

path

include Path to the folder containing the raw results of the run. Default is the current working directory. .

...

Not currently used.

Details

Creation of new PM_op object is automatic at the end of a run and not generally necessary for the user to do.

Method plot()

Plot method

Usage

PM_op$plot(...)

Arguments

...

Arguments passed to plot.PM_op

Details

See plot.PM_op.

Method summary()

Summary method

Usage

PM_op$summary(...)

Arguments

...

Arguments passed to summary.PM_op

Details

See summary.PM_op.

Method auc()

Calculate AUC

Usage

PM_op$auc(...)

Arguments

...

Arguments passed to makeAUC

data

The object to use for AUC calculation

Details

See makeAUC

Method clone()

The objects of this class are cloneable with this method.

Usage

PM_op$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.