doit_propose_new: Propose a new design point for the DoIt approximation
In sieste/doit: Bayesian Computation using Design of Experiments-Based Interpolation Technique
Description
Usage
Arguments
Value
Examples
Description
Find the point with largest estimation variance by numerical optimisation,
using the current design point with largest leave-one-out prediction error
as the starting point.
Usage
1
doit_propose_new(doit, theta_0 = NULL)
Arguments
doit
An object of class 'doit', see function 'doit_fit'.
theta_0
Data frame or vector. Initial value used by the optimisation
routine. Ignored for 1d problems.
Value
A parameter value.
Examples
1
2
3
4
design = data.frame(x=rnorm(10), y=rnorm(10))
design$f = with(design, exp(-0.5*(x+y)^2))
fit = doit_fit(design)
theta_new = doit_propose_new(fit)
sieste/doit documentation built on May 9, 2019, 4:10 p.m.
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Description Usage Arguments Value Examples
Description
Find the point with largest estimation variance by numerical optimisation, using the current design point with largest leave-one-out prediction error as the starting point.
Usage
1 | doit_propose_new(doit, theta_0 = NULL)
|
Arguments
doit |
An object of class 'doit', see function 'doit_fit'. |
theta_0 |
Data frame or vector. Initial value used by the optimisation routine. Ignored for 1d problems. |
Value
A parameter value.
Examples
1 2 3 4 | design = data.frame(x=rnorm(10), y=rnorm(10))
design$f = with(design, exp(-0.5*(x+y)^2))
fit = doit_fit(design)
theta_new = doit_propose_new(fit)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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