blackbox (version 1.0)

init_grid: Define starting points in parameter space.

Description

This function samples the space of estimated parameters. Also handles 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 is crude but achieves three desirable effects: It tries to sample the space uniformly, avoiding large gaps; it is not exactly a regular grid; and it includes replicates of some parameter points, required for good smoothing of the likelihood surface.

Usage

init_grid(lower=c(par=0), upper=c(par=1), steps=NULL, 
          nUnique=NULL, nRepl=min(10L,nUnique),
          jitterFac=0.5
         )

Arguments

lower

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.

upper

A vector of upper bounds for the parameters, as well as fixed parameters. Elements must be named and match those of lower.

steps

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

nUnique

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.

nRepl

Number of replicates of distinct values of parameter vectors in output.

jitterFac

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

Value

• A data frame. Each row defines a list of arguments of vector of the function simulating the summary statistics.

Examples

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)