Parse Pmetrics IT2B OutputSource:
ITparse processes the output from an IT2B run into a list.
This is the filename of the output from IT2B. Typically, the file will be called IT_RF0001.txt, and this is the default.
The output of
ITparse is a list with the following objects and
of the class IT2B.
Number of subjects
Number of random variables or parameters in the model
Number of fixed variables or parameters in the model
Names of random parameters
Names of fixed parameters
Names of covariates
Suggested boundaries for each random parameter to be passed to NPAG
Index of variables fixed to be positive
Values for fixed parameters
Maximum number of cycles specified by the user
Number of cycles run. If less than
icycmax, convergence occurred.
Stopping tolerance for convergence, default 0.001
Boolean value if convergence occurred.
Ordindary Differential Equation solver tolerance.
Number of output equations
Vector of length equal to
numeqtwhose values are 0 if gamma was estimated for that output equation or 1 if gamma was fixed to 1 for that output equation
Number of drug inputs
Vector of values of the salt fraction for each
Vector of the number of doses for each subject in the population
Number of covariates in the model
Vector of the number of observations for each subject in the population
Maximum number of observation in any individual subject
Array of population model predictions for each subject at each observation time point. ypredpopnsub,numeqt,time,type where type is 1=mean, 2=median of the population prior used to calculate ypredpop
Array of Bayesian posterior model predictions for each subject at each observation time point. ypredbaynsub,numeqt,time,type where type is 1=mean, 2=median of the population prior used to calculate ypredbay
Array of Bayesian posterior parameter estimates for each subject, parbaynsub,nvar,type where type is 1=mean, 2=median of the population prior used to calculate parbay
Data frame with one row and two columns for final cycle Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)
Vector of cycle number and associated log-likelihood
Matrix of cycle numbers and associated means for each random parameter
Matrix of cycle numbers and associated medians for each random parameter
Matrix of cycle numbers and associated standard deviations for each random parameter
Matrix of cycle numbers and associated coefficients of variation for each random parameter
Matrix of cycle number and associated gamma or lambda with each output equation in a column
Matrix of subjects in rows and MAP Bayesian parameter estimates in columns for each parameter, based on population means from the next to last cycle.
Matrix of subjects in rows and SD of Bayesian posterior parameter distributions in columns for each parameter, based on population means from the next to last cycle.
Matrix of subjects in rows and CV of Bayesian posterior parameter distributions in columns for each parameter, based on population means from the next to last cycle.
Subject data consisting of 5 columns: id, nsub, age, sex, ht, id is the original identification number in the .csv matrix file; nsub is the sequential subject number in the IT2B run; age, sex and ht will be missing for .csv input and present if included in .wrk input files
Data frame with all dosing information for each subject, including times, routes, amounts, and associated covariate values
Data frame with measured outputs for each subject and associated assay error polynomials. The order of the columns is nsub, time, numeqt, observation, c0, c1, c2, c3, where the last four columns are the coefficients of the assay error polynomial for that observation, such that SDobs = c0 + c1*obs + c2*obs*2 + c3obs**3
A flag indicating that some negative predictions were changed to missing. This means that the model may be misspecified.
The filename of the data used in the run.
This function can take some time to process the RFILE, depending on the number of subjects, doses, observations, etc. Typical wait times are a few seconds up to 5 minutes. When processing is complete a summary of the extracted data will be reported on the console.