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.
1 2 3 4 |
problem |
A list containing the following levels:
|
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 |
files |
A list of input used for the analysis. The following elements are expected and none of them could be null:
|
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)
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