latin.hypercube

Character vector for column names (optional)

names

Boolean variable with <code>TRUE</code> meaning to
 normalize each column so the minimum is zero and the maximum is
 one. If it takes its default <code>FALSE</code>, the points represent
 midpoints of \(n\) equispaced intervals; the points thus have a minimum of
 \(0.5/n\) and a maximum of \(1-0.5/n\)

normalize

Boolean with default <code>FALSE</code> meaning to return a
 complex latin hypercube in which real and imaginary components separately
 form a latin hypercube

complex

Gives a Latin hypercube design matrix with an arbitrary number of
 points in an arbitrary number of dimensions. The <code>toy</code> dataset
 was generated using <code>latin.hypercube()</code>.

models


Allows one to estimate the output of a computer program,
as a function of the input parameters, without actually running it.
The computer program is assumed to be a Gaussian process, whose
parameters are estimated using Bayesian techniques that give a PDF of
expected program output.  This PDF is conditional on a training set
of runs, each consisting of a point in parameter space and the model
output at that point.  The emphasis is on complex codes that take
weeks or months to run, and that have a large number of undetermined
input parameters; many climate prediction models fall into this
class.  The emulator essentially determines Bayesian posterior
estimates of the PDF of the output of a model, conditioned on results
from previous runs and a user-specified prior linear model.  A vignette is
provided and the help pages include examples.

latin.hypercube: Latin hypercube design matrix

Description

Usage

Arguments

Examples