| shin92protoalcove_opt {catlearn} | R Documentation |
Parameter optimization of proto-ALCOVE model with shin92 CIRP
Description
Uses shin92protoalcove to find best-fitting parameters for
the proto-ALCOVE model for the shin92 CIRP.
Usage
shin92protoalcove_opt(params = c(2,1,.25,.75), recompute = FALSE,
trace = 0)
Arguments
params |
A vector containing the initial values for c, phi, la,
and lw, in that order. See |
recompute |
When set to TRUE, the function re-runs the optimization (which takes about 10 minutes on a 2.4 GHz processor). When set to FALSE, the function returns a stored copy of the results of the optimization (which is instantaneous). |
trace |
Sets the level of tracing information (i.e. information
about the progress of the optimization), as defined by the
|
Details
This function is an archive of the optimization procedure used to
derive the best-fitting parameters for the shin92protoalcove
simulation; see Spicer et al. (2017) for a tutorial introduction to
the concept of simulation archives.
Optimization used the L-BFGS-B method from the optim
function of the standard R stats package. The objective
function was sum of squared errors. Please inspect the source code for
further details (e.g. type shin92protoalcove_opt).
This function was run in 16 times from different starting points, using 8 threads on a Core i7 3.6 GHz processor. The default parameters of this function are those for the best fit from those 16 starting points. The 16 starting points were
pset <- rbind(
c(2,1,.25,.25),c(2,1,.25,.75),c(2,1,.75,.25),c(2,1,.75,.75),
c(2,3,.25,.25),c(2,3,.25,.05),c(2,3,.75,.25),c(2,3,.75,.75),
c(8,1,.25,.25),c(8,1,.25,.75),c(8,1,.75,.25),c(8,1,.75,.75),
c(8,3,.25,.25),c(8,3,.25,.75),c(8,3,.75,.25),c(8,3,.75,.75)
)
not all of which converged successfully.
Value
A vector containing the best-fitting values for c, phi, la,
and lw, in that order. See slpALCOVE for an explanation
of these parameters.
Author(s)
Andy Wills
References
Spicer, S., Jones, P.M., Inkster, A.B., Edmunds, C.E.R. & Wills, A.J. (2017). Progress in learning theory through distributed collaboration: Concepts, tools, and examples. Manuscript in preparation.