These functions sample the space of estimated parameters, and also handle other fixed arguments that need to be passed to the function simulating the summary statistics (sample size is likely to be one such argument). The current sampling strategy of these functions is crude but achieves desirable effects for present applications: it samples the space more uniformly, by generating fewer pairs of close points than independent sampling of each point would; it is not exactly a regular grid; and init_grid generates replicates of a few parameter points, which were required in the primitive workflow for good smoothing of the likelihood surface. init_reftable is a trivial wrapper around init_grid, setting the number of replicates to zero, which is appropriate in up-to-date workflows.
init_reftable(lower=c(par=0), upper=c(par=1), steps=NULL,
nUnique=NULL, maxmin=TRUE, jitterFac=0.5)
init_grid(lower=c(par=0), upper=c(par=1), steps=NULL, nUnique=NULL,
nRepl=min(10L,nUnique), maxmin=TRUE, jitterFac=0.5)A data frame. Each row defines a list of arguments of vector of the function simulating the summary statistics.
A vector of lower bounds for the parameters, as well as fixed arguments to be passed to the function simulating the summary statistics. Elements must be named. Fixed parameters character strings.
A vector of upper bounds for the parameters, as well as fixed parameters. Elements must be named and match those of lower.
Number of steps of the grid, in each dimension of estimated parameters. If NULL, a default value is defined from the other arguments. If a single value is given, it is applied to all dimensions. Otherwise, this must have the same length as lower and upper and named in the same way as the variable parameters in these arguments.
Number of distinct values of parameter vectors in output. Default is an heuristic guess for good start from not too many points, computed as floor(50^((v/3)^(1/3))) where v is the number of variable parameters.
Number of replicates of distinct values of parameter vectors in output.
Boolean. If TRUE, use a greedy max-min strategy (GMM, inspired from Ravi et al. 1994) in the selection of points from a larger set of points generated by an hypercube-sampling step. If FALSE, sample is instead used for this second step. This may be useful as the default method becomes slow when thousands of points are to be sampled.
Controls the amount of jitter of the points around regular grid nodes. The default value 0.5 means that a mode can move by up to half a grid step (independently in each dimension), so that two adjacent nodes moved toward each other can (almost) meet each other.
Ravi S.S., Rosenkrantz D.J., Tayi G.K. 1994. Heuristic and special case algorithms for dispersion problems. Operations Research 42, 299-310.
set.seed(123)
init_grid()
init_grid(lower=c(mu=2.8,s2=0.5,sample.size=20),
upper=c(mu=5.2,s2=4.5,sample.size=20),
steps=c(mu=7,s2=9),nUnique=63)
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