Observed vs. predicted dataSource:
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.
allows you to access them in an S3 way, e.g.
run1 is a
However, if you wish to manipulate the entire data frame,
data field, e.g.
trough <- run1$op$data %>% filter(time == 24). If
you are unfamiliar with the
%>% pipe function, please type
into the R console and look online for instructions/tutorials in tidyverse, a
powerful approach to data manipulation upon which Pmetrics is built.
observation time in relative units, usually hours
Population predictions based on Bayesian prior parameter value distribution, or individual predictions based on Bayesian posterior parameter value distributions
Predictions based on mean or median of Bayesian
output equation number
dosing block number for each subject, as defined by dose resets (evid=4).
standard deviation of the observation based on the assay error polynomial
squared prediction error
weighted prediction error, which is the prediction error divided by the
weighted squared prediction error
A data frame combining all the above fields as its columns
Create new object populated with observed vs. predicted data
The parsed output from makeOP.
Arguments passed to plot.PM_op
Arguments passed to summary.PMop
Arguments passed to makeAUC
The object to use for AUC calculation