## Arguments

- outfile
This is the filename of the output from IT2B. Typically, the file will be called IT_RF0001.txt, and this is the default.

## Value

The output of `ITparse`

is a list with the following objects and
of the class *IT2B*.

- nsub
Number of subjects

- nvar
Number of random variables or parameters in the model

- nofix
Number of fixed variables or parameters in the model

- par
Names of random parameters

- parfix
Names of fixed parameters

- covnames
Names of covariates

- ab
Suggested boundaries for each random parameter to be passed to NPAG

- fixedpos
Index of variables fixed to be positive

- valfix
Values for fixed parameters

- icycmax
Maximum number of cycles specified by the user

- icyctot
Number of cycles run. If less than

`icycmax`

, convergence occurred.- stoptol
Stopping tolerance for convergence, default 0.001

- converge
Boolean value if convergence occurred.

- ODEtol
Ordindary Differential Equation solver tolerance.

- numeqt
Number of output equations

- ERRmod
Vector of length equal to

`numeqt`

whose values are 0 if gamma was estimated for that output equation or 1 if gamma was fixed to 1 for that output equation- ndrug
Number of drug inputs

- salt
Vector of values of the salt fraction for each

`ndrug`

- ndose
Vector of the number of doses for each subject in the population

- ncov
Number of covariates in the model

- nobs
Vector of the number of observations for each subject in the population

- nobsmax
Maximum number of observation in any individual subject

- ypredpop
Array of population model predictions for each subject at each observation time point.

`ypredpop[nsub,numeqt,time,type`

where*type*is 1=mean, 2=median of the population prior used to calculate ypredpop- ypredbay
Array of Bayesian posterior model predictions for each subject at each observation time point.

`ypredbay[nsub,numeqt,time,type`

where*type*is 1=mean, 2=median of the population prior used to calculate ypredbay- parbay
Array of Bayesian posterior parameter estimates for each subject,

`parbay[nsub,nvar,type`

where*type*is 1=mean, 2=median of the population prior used to calculate parbay- ic
Data frame with one row and two columns for final cycle Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)

- ilog
Vector of cycle number and associated log-likelihood

- imean
Matrix of cycle numbers and associated means for each random parameter

- imed
Matrix of cycle numbers and associated medians for each random parameter

- isd
Matrix of cycle numbers and associated standard deviations for each random parameter

- icv
Matrix of cycle numbers and associated coefficients of variation for each random parameter

- igamlam
Matrix of cycle number and associated gamma or lambda with each output equation in a column

- lpar
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.

- lsd
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.

- lcv
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.

- sdata
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- dosecov
Data frame with all dosing information for each subject, including times, routes, amounts, and associated covariate values

- outputs
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

`SD[obs] = c0 + c1*[obs] + c2*[obs]**2 + c3*[obs]**3`

- negflag
A flag indicating that some negative predictions were changed to missing. This means that the model may be misspecified.

- mdata
The filename of the data used in the run.