maximizeOK

Assuming that <code>calcPredictorOK</code> has been first run to produce a predictor of the response surface, this finds its constrained maximum in the convex envelope of the smoothed data.

Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.

Franois Rousset

blackbox

Black Box Optimization and Exploration of Parameter Space

François Rousset

Raphaël Leblois

maximizeOK function

<dl> <dt>fitobject</dt>
<dd>Return object of <code>calcPredictorOK</code>.</dd> <dt>cleanResu</dt>
<dd>A connection, or a character string naming a file for some nicely formated output. If <code>""</code> (the default), print to the standard output connection.</dd></dl>

Arguments

Find maximum of predicted response surface — maximizeOK

<dl>

 <dt>fitobject</dt>
<dd>Return object of <code>calcPredictorOK</code>.</dd>

 <dt>cleanResu</dt>
<dd>A connection, or a character string naming a file for some nicely formated output. If <code>""</code> (the default), print to the standard output connection.</dd>

</dl>

maximizeOK: Find maximum of predicted response surface

Description

Usage

Value

Arguments