problem |
A list containing the following levels:
- data
A list which content depends on the scope of the analysis. If
the analysis was run at the level of the subject, data contains as
many levels as the number of subjects in the dataset, plus the ids
level containing the vector of identification numbers of all subjects
included in the analysis population. If the analysis was run at the level
of the population, data contains only one level of data and
ids is set to 1.
Each subject-specific level contains as many levels as there are treatment
levels for this subject, plus the trts level listing all treatments
for this subject, and the id level giving the identification number
of the subject.
Each treatment-specific levels is a list containing the following levels:
- cov
mij x 3 data.frame containing the times of observations of the
dependent variables (extracted from the TIME variable), the indicators
of the type of dependent variables (extracted from the CMT variable),
and the actual dependent variable observations (extracted from the
DV variable) for this particular treatment and this particular
subject.
- cov
mij x c data.frame containing the times of observations of
the dependent variables (extracted from the TIME variable) and all the
covariates identified for this particular treatment and this
particular subject.
- bolus
bij x 4 data.frame providing the instantaneous inputs
for a treatment and individual.
- infusion
fij x (4+c) data.frame providing the zero-order inputs for
a treatment and individual.
- trt
the particular treatment identifier.
- method
A character string, indicating the scale of the analysis. Should
be 'population' or 'subject'.
- init
A data.frame of parameter data with the following columns:
'names', 'type', 'value', 'isfix', 'lb', and 'ub'.
- debugmode
Logical indicator of debugging mode.
- modfun
Model function.
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Fit |
A list containing the following elements:
- estimations
The vector of final parameter estimates.
- fval
The minimal value of the objective function.
- cov
The matrix of covariance for the parameter estimates.
- orderedestimations
A data.frame with the same structure as
problem$init but only containing the sorted estimated estimates.
The sorting is performed by order.param.list.
- cor
The upper triangle of the correlation matrix for the parameter
estimates.
- cv
The coefficients of variations for the parameter estimates.
- ci
The confidence interval for the parameter estimates.
- AIC
The Akaike Information Criterion.
- sec
A list of data related to the secondary parameters, containing
the following elements:
- estimates
The vector of secondary parameter estimates calculated
using the initial estimates of the primary model parameters.
- names
The vector of names of the secondary parameter estimates.
- pder
The matrix of partial derivatives for the secondary
parameter estimates.
- cov
The matrix of covariance for the secondary parameter
estimates.
- cv
The coefficients of variations for the secondary parameter
estimates.
- ci
The confidence interval for the secondary parameter
estimates.
- orderedfixed
A data.frame with the same structure as
problem$init but only containing the sorted fixed estimates.
The sorting is performed by order.param.list.
- orderedinitial
A data.frame with the same content as
problem$init but sorted by order.param.list.
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files |
A list of input used for the analysis. The following elements are
expected and none of them could be null:
- data
A .csv file located in the working directory, which contains
the dosing information, the observations of the dependent variable(s)
to be modeled, and possibly covariate information. The expected format
of this file is described in details in vignette('scaRabee',
package='scaRabee').
- param
A .csv file located in the working directory, which contains
the initial guess(es) for the model parameter(s) to be optimized or used
for model simulation. The expected format of this file is described in
details in vignette('scaRabee',package='scaRabee').
- model
A text file located in the working directory, which defines
the model. Models specified with explicit, ordinary or delay
differential equations are expected to respect a certain syntax and
organization detailed in vignette('scaRabee',package='scaRabee').
- iter
A .csv file reporting the values of the objective function
and estimates of model parameters at each iteration.
- report
A text file reporting for each individual in the dataset the
final parameter estimates for structural model parameters, residual
variability and secondary parameters as well as the related statistics
(coefficients of variation, confidence intervals, covariance and
correlation matrix).
- pred
A .csv file reporting the predictions and calculated residuals
for each individual in the dataset.
- est
A .csv file reporting the final parameter estimates for each
individual in the dataset.
- sim
A .csv file reporting the simulated model predictions for each
individual in the dataset. (Not used for estimation runs).
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