candexch
D-optimal design from candidate set using row exchanges
Syntax
rlist = candexch(C,nrows)
rlist = candexch(C,nrows,Name,Value)
Description
uses
a row-exchange algorithm to select a D-optimal
design from the candidate set rlist
= candexch(C
,nrows
)C
.
generates
a D-optimal design with additional options specified
by one or more rlist
= candexch(C
,nrows
,Name,Value
)Name,Value
pair arguments.
Input Arguments
|
|
|
The desired number of rows in the design. |
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
|
When Default: |
|
Default: A random subset of the rows of
|
|
Maximum number of iterations, a positive integer. Default: |
|
A structure that specifies whether to run in parallel, and specifies the random stream or streams. Parallel computation requires Parallel Computing Toolbox™. Create the
Default: |
|
An
Default: |
|
Number of times to try to generate a design from a new starting point. The algorithm uses random points for each try, except possibly the first. Default: |
Output Arguments
|
Vector of length |
Examples
This example shows how to generate a D-optimal
design when there is a restriction on the candidate set, so the rowexch
function
isn't appropriate.
F = (fullfact([5 5 5])-1)/4; % factor settings in unit cube T = sum(F,2)<=1.51; % find rows matching a restriction F = F(T,:); % take only those rows C = [ones(size(F,1),1) F F.^2]; % compute model terms including % a constant and all squared terms R = candexch(C,12); % find a D-optimal 12-point subset X = F(R,:); % get factor settings
Algorithms
candexch
selects a starting design X
at
random, and uses a row-exchange algorithm to iteratively replace rows
of X
by rows of C
in an attempt
to improve the determinant of X'*X
.
Alternatives
The rowexch
function also
generates D-optimal designs using a row-exchange
algorithm, but it automatically generates a candidate set that is
appropriate for a specified model. The daugment
function
augments a set of fixed design points using a coordinate-exchange
algorithm; the 'start'
parameter provides the same
functionality using the row exchange algorithm.
Extended Capabilities
Version History
Introduced before R2006a