Parse Pmetrics NPAG OutputSource:
NPparse processes the output from an NPAG run into a list.
This is the filename of the output from NPAG. Typically, the file will be called NP_RF0001.txt, and this is the default.
The output of
NPparse is a list with the following objects and
of the class NPAG.
Number of subjects
Number of active grid points at the final cycle
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
Initial boundaries for each random parameter
Values for fixed parameters
Number of differential equations in model, or 0 for only output equation, or -1 for analytic solution (algabraic)
Index for the initial number of gridpoints in the model
Starting cycle number
Maximum number of cycles specified by the user
Number of cycles run. If less than
icycmax, convergence occurred.
Boolean value if convergence occurred.
Ordindary Differential Equation solver tolerance.
Prior density for the run, either “UNIFORM” or the name of the user-specified density file, typically “DEN0001”.
Assay error model: 1 for SD; 2 for \(SD*gamma\); 3 for additive lambda model; and 4 for gamma only
Number of output equations
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
Vector of the number of time points for each subject at which a prediction is generated for each numeqt output equation
Final cycle joint population density of parameter estimates
Array of posterior parameter value distributions for the first 100 subjects at each observation time point. postden[nsub,nactvepost,density] where nactvepost is the posterior grid point
Matrix of posterior probability of each nactve point for each subject, given that subject's data
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, 3=mode of the population prior used to calculate ypredpop
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, 3=mode of the population prior used to calculate ypredbay
Matrix of the prediction time points for each subject, with nsub rows and max(numt) columns
Array of the mean, median, and mode of the posterior marginal distribution for each parameter in each subject, of the form exx[nvar,type,nsub]
Array of population model predictions for each subject at each ttpred time point, of the form ypredpopt[nsub, numeqt, time, type], where type is 1=mean, 2=median, 3=mode of the population prior used to calculate ypredpopt
Matrix of cycle number and associated log-likelihood
Matrix with cycle number and Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for each cycle
Matrix of cycle numbers and associated means for each random parameter
Matrix of cycle numbers and associated standard deviations for each random parameter
Array of additional information for each random parameter in each cycle, of the form iaddl[info, nvar, cycle], where info is a value from 1 to 12: 1= mode; 2= skewness; 3= kurtosis; 4-8 give percentiles of the distribution where 4=2.5%; 5=25%; 6=50% (median); 7=75%; 8=97.5%; 9= the standard deviation of a normal distribution with the same interquartile range; 10=the standard deviation of a normal distribution with the same 95% range; 11=the average of 9 and 10; 12=the % scaled information
Matrix of cycle number and associated gamma or lambda
Vector of each subject's Bayesian posterior log-likelihood
Matrix of subject numbers and associated Bayesian posterior means for each random parameter
Matrix of subject numbers and associated Bayesian posterior standard deviations for each random parameter
Array of Bayesian posterior additional information for each random parameter for each subject, of the form baddl[info, nvar, nsub], where info is the same as for iaddl.
Matrix of AUC blocks for each subject with 5 columns: [nsub, numeqt, nblock, tau, auc]; nsub and numeqt are as previously defined; nblock is the AUC block as defined by successive dose reset (evid=4) events; tau is the time interval for that block; auc is the AUC for that block
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 NPAG run; age, sex and ht will be missing for .csv input and present if included in .wrk input files
Matrix with all dosing information for each subject, including times, routes, amounts, and associated covariate values
Matrix 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 \(SD[obs] = c0 + c1*[obs] + c2*[obs]**2 + c3*[obs]**3\)
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.