Doptimal design from candidate set using row exchanges
rlist = candexch(C,nrows)
rlist = candexch(C,nrows,Name,Value)
uses
a rowexchange algorithm to select a Doptimal
design from the candidate set rlist
= candexch(C
,nrows
)C
.
generates
a Doptimal design with additional options specified
by one or more rlist
= candexch(C
,nrows
,Name,Value
)Name,Value
pair arguments.



The desired number of rows in the design. 
Specify optional commaseparated pairs of Name,Value
arguments.
Name
is the argument
name and Value
is the corresponding
value. Name
must appear
inside single quotes (' '
).
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN
.

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. 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: 

Vector of length 
This example shows how to generate a Doptimal
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 Doptimal 12point subset X = F(R,:); % get factor settings
The rowexch
function also
generates Doptimal designs using a rowexchange
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 coordinateexchange
algorithm; the 'start'
parameter provides the same
functionality using the row exchange algorithm.