Specifies control parameters for the model estimation process in
[estimate].
set_estimation_opt(
initial_parameters = NULL,
fixed_parameters = NULL,
max_iterations = 20,
convergence_criterion = 0.001,
initial_damping = NULL,
damping_increase_factor = 2,
damping_decrease_factor = 3,
return_interval_loglik = FALSE,
return_probabilities = FALSE,
engine = c("default_c", "default", "gather_compute")
)A numeric vector. It includes initial parameter
values used to initialize the estimation process.
Default is NULL, which means parameters are initialized at zero,
except for the rate intercept when present.
A numeric vector of the same length
as the number of parameters to be estimated in the model.
NA values indicate parameters to be estimated,
while numeric values indicate parameters to be fixed at the given value.
For example, if the vector is c(2, NA) then the first component of the
parameter is fixed to 2 during the estimation process.
Default is NULL (all parameters are estimated).
An integer.
The maximum number of iterations in the Gauss-Fisher scoring algorithm.
Default is 20.
A numeric value.
The convergence criterion for the estimation.
The algorithm stops if the sum of absolute scores is smaller than
this value.
Default is 0.001.
A numeric value.
The initial damping factor for the Gauss-Fisher scoring algorithm.
Default is NULL, which allows estimate_dynam(), estimate_rem() and
estimate_dynami() to set
a context-dependent default
(e.g., 30 or 10 based on wheter the model has windows effects).
If set, this value is used directly.
A numeric value.
Factor by which damping is increased when improvements
in the estimation are found. Must be >= 1. Default is 2.
A numeric value.
Factor by which damping is decreased when no improvements
in the estimation are found. Must be >= 1. Default is 3.
A logical value.
Whether to keep and return the
log-likelihood for each event. Default is FALSE.
A logical value. Whether to keep and return the probabilities for all alternatives for each event.
When subModel = "choice" the probabilities correspond to all actors in
the choice set present at the time of the event.
When model = "REM" the probabilities correspond to all dyads present at
the time of the event.
Default is FALSE.
A character string specifying the estimation engine. Options are:
C++ based implementation using RcppEigen
and RcppParallel.
R-based implementation.
C++ based implementation with a different data
structure that reduces the time but it can increase the memory usage.
Default is "default_c".
An object of class estimation_opt.goldfish (a list object),
where the components values are the default values or the values provided
to the function. The list object has the following components:
Initial parameter values used during the estimation process.
Values for parameters fixed during the estimation process.
Maximum number of iterations in the estimation process.
Convergence criterion for the estimation process.
Initial damping factor for the estimation process.
Factor by which damping is increased when improvements in the estimation are found.
Factor by which damping is decreased when no improvements in the estimation are found.
Logical value indicating whether to return the log-likelihood for each event.
Estimation engine used in the estimation process.
The damping factors arguments control the step size at each iteration of
the Newton-Raphson algorithm. They have a bigger impact in the first
iterations of the algorithm and will decrease by half after each iteration.
In particular, the increase factor is the one that is expected to play
a role during the first iterations where it's easier to improve
the log-likelihood.
In scenarios where the model is fit in a large dataset, for example,
when the number of actors in the system is large,
the damping_increase_factor and the damping_decrease_factor arguments
can be increased from the default values (e.g., 4 or 6) to speed up the
estimation process with large changes in the coefficients.
However, this should have the opposite effect in small datasets producing
large changes in the coefficients that would create more iterations
(in a similar vein to the step size parameter in gradient descendent).
est_ctrl <- set_estimation_opt(
max_iterations = 50,
convergence_criterion = 1e-4
)