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candexch

D-optimal design from candidate set using row exchanges

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Syntax

rlist = candexch(C,nrows)
rlist = candexch(C,nrows,Name=Value)

Description

rlist = candexch(C,nrows) uses a row-exchange algorithm to select a D-optimal design from the candidate set C.

example

rlist = candexch(C,nrows,Name=Value) generates a D-optimal design with additional options specified by one or more name-value arguments.

Examples

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Open Live Script

Define a candidate set with a restriction.

F = (fullfact([5 5 5])-1)/4; % Factor settings in a 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];

Calculate model terms including a constant and all squared terms. Because the candidate set has a restriction, use the candexch function instead of the rowexch function.

R = candexch(C,12);         % Find a D-optimal 12-point subset
X = F(R,:);                 % Get factor settings

Input Arguments

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Candidate set, specified as a matrix of size N-by-P. The candidate set contains the values of P model terms at each of N points.

Desired number of rows in the design, specified as a positive integer.

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.

Example: rlist = candexch(C,nrows,MaxIterations=7) specifies the maximum number of iterations as 7.

Flag for candexch to avoid calculating duplicate rows, specified as a numeric or logical 1 (true) or 0 (false). If AvoidDuplicates is true and candexch is able to calculate non-duplicate points, the rows of dRE are unique. When AvoidDuplicates is false, the function does not avoid calculating duplicate rows.

Example: AvoidDuplicates=true

Data Types: single | double | logical

Display the iteration number, specified as "on" or "off". By default, the candexch function displays the iteration number, except when the UseParallel field of the Options name-value argument is true.

Initial design, specified as a numeric matrix of size nrows-by-P. By default, the candexch function uses a random subset of the rows of C as the initial design.

Maximum number of iterations, specified as a positive integer.

Options for computing in parallel and setting random streams, specified as a structure. Create the Options structure using statset. This table lists the option fields and their values.

Field NameValueDefault
UseParallelSet this value to true to run computations in parallel.false
UseSubstreams

Set this value to true to run computations in a reproducible manner.

To compute reproducibly, set Streams to a type that allows substreams: "mlfg6331_64" or "mrg32k3a".

false
StreamsSpecify this value as a RandStream object or cell array of such objects. Use a single object except when the UseParallel value is true and the UseSubstreams value is false. In that case, use a cell array that has the same size as the parallel pool.If you do not specify Streams, then candexch uses the default stream or streams.

Note

You need Parallel Computing Toolbox™ to run computations in parallel.

Example: Options=statset(UseParallel=true,UseSubstreams=true,Streams=RandStream("mlfg6331_64"))

Data Types: struct

Factor settings, specified as a numeric matrix of size nobs-by-P, where nobs is the number of fixed design points. candexch finds nrows additional rows to add to the FixedRows design.

Number of tries to generate a design from a new starting point, specified as a positive integer. The algorithm uses random points for each try, except possibly the first.

Output Arguments

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Selected rows, returned as a vector of length nrows.

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 FixedRows name-value argument provides the same functionality using the row exchange algorithm.

Extended Capabilities

Version History

Introduced before R2006a

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See Also

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