Description
Usage
Arguments
Details
Value
Author(s)
See Also
View source: R/scarabee.gridsearch.R
scarabee.gridsearch
is a secondary function called during direct grid
search runs. It creates a matrix made of unique vectors of parameter
estimates set around the vector of initial estimates and evaluates the
objective function (i.e. minus twice the log of the exact likelihood of the
observed data, given the structural model, the model of residual variability,
and the vector of parameter estimates) at each of those vectors at the
population level. The grid of objective function values is then sorted and the
best vector is used to simulate the model at the population level.
scarabee.gridsearch
is typically not called directly by users.
 (problem = ,
npts = ,
alpha = ,
= )

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 subjectspecific 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 treatmentspecific 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 zeroorder 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.

npts 
An integer greater than 2, defining the number of points that the
grid should contain per dimension (i.e variable model parameter).

alpha 
A vector of numbers greater than 1, which give the factor(s) used
to calculate the evaluation range of each dimension of the search grid (see
Details). If alpha length is lower than the number of variable
parameters, elements of alpha are recycled. If its length is higher
than number of variable parameters, alpha is truncated.

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. (Not used for
direct grid search runs).
 report
A text file reporting the summary tables of ordered
objective function values for the various tested vectors of model
parameters.
 pred
A .csv file reporting the predictions and calculated residuals
for each individual in the dataset. (Not used for direct grid search
runs).
 est
A .csv file reporting the final parameter estimates for each
individual in the dataset. (Not used for direct grid search runs).
 sim
A .csv file reporting the simulated model predictions for each
individual in the dataset. (Not used for direct grid search runs).

The actual creation of the grid and the evaluation of the objective function
is delegated by scarabee.gridsearch
to the fmin.gridsearch
function of the neldermead package.
This function evaluates the cost function  that is, in the present case, the
objective function  at each point of a grid of npts^length(x0)
points,
where x0
is the vector of model parameters set as variable. If
alpha
is NULL, the range of the evaluation points is limited by the
lower and upper bounds of each parameter of x0
provided in the
parameter file. If alpha
is not NULL, the range of the evaluation
points is defined as [x0/alpha,x0*alpha]
.
Because fmin.gridsearch
can be applied to the evaluation of constrained
systems, it also assesses the feasibility of the cost function at each point
of the grid (i.e. whether or not the points satisfy the defined constraints).
In the context of scaRabee, the objective function is always feasible.
Return a data.frame with pe+2 columns. The last 2 columns report the value
and the feasibility of the objective function at each specific vector of
parameter estimates which is documented in the first pe columns. This
data.frame is ordered by feasibility and increasing value of the objective
function.
Sebastien Bihorel (sb.pmlab@gmail.com)
fmin.gridsearch
scaRabee documentation built on Feb. 4, 2022, 5:07 p.m.