rowexch
Row exchange D-optimal design
Syntax
Description
uses a row-exchange algorithm (see Algorithms) to generate a D-optimal design
dRE
= rowexch(nfactors
,nruns
)dRE
with nruns
runs (the rows of
dRE
) for a linear additive model with nfactors
factors (the columns of dRE
). The model includes a constant
term.
specifies options using one or more name-value arguments in addition to any of the input
argument combinations in the previous syntaxes. For example, you can specify the maximum
number of start points for generating the design, and whether to perform computations in
parallel.dRE
= rowexch(___,Name=Value
)
Examples
Input Arguments
Name-Value Arguments
Output Arguments
Algorithms
Both cordexch
and rowexch
use
iterative search algorithms that incrementally change an initial design matrix
X to increase D =
|XTX|
at each iteration. In both algorithms, randomness is built into the selection of the initial
design and the choice of the incremental changes. As a result, both algorithms might return
locally D-optimal designs instead of globally D-optimal designs. Run each algorithm multiple
times and select the best result for your final design. To automate this repetition and
comparison, set the NumTries
name-value argument of either
function.
At each iteration, the row-exchange algorithm exchanges an entire row of
X with a row from a design matrix C evaluated at a
candidate set of feasible treatments. The rowexch
function automatically
generates a design matrix that is appropriate for the specified model, operating in two steps
by calling the candgen
and candexch
functions in sequence. Provide your own C by calling
candexch
directly. In either case, if C is large, its static
presence in memory might affect the computation speed.