estimation.plot: Create Summary Estimation Plots
In scaRabee: Optimization Toolkit for Pharmacokinetic-Pharmacodynamic Models

estimation.plot is a secondary function called at the end of the estimation runs. It generates plots from the iteration log file and the prediction & residual file. Those plots are: a figure summarizing the changes in the objective function and the estimated parameter values as a function of the iteration plus, for each subject and sub-problem (i.e. treatment), a figure overlaying model predictions and observed data, and another figure showing 4 goodness-of-fit plots (predictions vs observations, weighted residuals vs time, weighted residuals vs observations, weighted residuals vs predictions). See vignette('scaRabee',package='scaRabee') for more details. estimation.plot is typically not called directly by users.

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  estimation.plot(problem = NULL,
                  Fit = NULL,
                  files = NULL)

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.

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.

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).