lhsdesign

Latin hypercube sample

Syntax

X = lhsdesign(n,p)

X = lhsdesign(n,p,Name,Value)

Description

X = lhsdesign(n,p) returns a Latin hypercube sample matrix of size n-by-p. For each column of X, the n values are randomly distributed with one from each interval (0,1/n), (1/n,2/n), ..., (1 - 1/n,1), and randomly permuted.

X = lhsdesign(n,p,Name,Value) modifies the resulting design using one or more name-value pair arguments. For example, you can obtain a discrete design by specifying 'Smooth','off'.

Examples

Create Latin Hypercube Sample

Create a Latin hypercube sample of 10 rows and 4 columns.

rng default % For reproducibility
X = lhsdesign(10,4)

X = 10×4

    0.1893    0.2569    0.0147    0.5583
    0.8038    0.1089    0.9378    0.1950
    0.5995    0.6818    0.3649    0.3097
    0.3225    0.8736    0.4487    0.8055
    0.9183    0.9854    0.1598    0.2509
    0.0131    0.3864    0.5924    0.7511
    0.7916    0.7131    0.2760    0.6662
    0.6600    0.5420    0.6877    0.9100
    0.2740    0.0450    0.7816    0.0631
    0.4200    0.4855    0.8760    0.4889

Each column of X contains one random number in each interval [0,0.1], [0.1,0.2], [0.2,0.3], [0.3,0.4], [0.4,0.5], [0.5,0.6], [0.6,0.7], [0.7,0.8], [0.8,0.9], and [0.9,1].

Latin Hypercube Design with Nondefault Options

Determine the effects of various name-value pair arguments in lhsdesign. Start with a default design for 10 rows and four columns.

rng default % For reproducibility
X = lhsdesign(10,4)

X = 10×4

    0.1893    0.2569    0.0147    0.5583
    0.8038    0.1089    0.9378    0.1950
    0.5995    0.6818    0.3649    0.3097
    0.3225    0.8736    0.4487    0.8055
    0.9183    0.9854    0.1598    0.2509
    0.0131    0.3864    0.5924    0.7511
    0.7916    0.7131    0.2760    0.6662
    0.6600    0.5420    0.6877    0.9100
    0.2740    0.0450    0.7816    0.0631
    0.4200    0.4855    0.8760    0.4889

To obtain a discrete design. as opposed to a continuous design, set the 'Smooth' name-value pair argument to 'off'.

rng default % For reproducibility
X = lhsdesign(10,4,'Smooth','off')

X = 10×4

    0.2500    0.3500    0.7500    0.8500
    0.1500    0.8500    0.2500    0.3500
    0.8500    0.7500    0.4500    0.7500
    0.9500    0.1500    0.6500    0.1500
    0.0500    0.0500    0.8500    0.9500
    0.4500    0.5500    0.9500    0.4500
    0.3500    0.9500    0.5500    0.0500
    0.5500    0.4500    0.0500    0.2500
    0.6500    0.6500    0.1500    0.6500
    0.7500    0.2500    0.3500    0.5500

The resulting design is discrete.

Calculate the sum of squares of the between-column correlations of the returned design.

y = corr(X);
(sum(y(:).^2) - 4)/2 % Subtract 4 to remove the diagonal terms of corr(X)

ans = 0.4874

Observe the effect of changing the 'Criterion' name-value pair argument to 'correlation', which minimizes the sum of between-column squared correlations. The 'correlation' criterion always gives a discrete design, as if 'Smooth' is set to 'off'.

rng default % For reproducibility
X = lhsdesign(10,4,'Criterion','correlation')

X = 10×4

    0.6500    0.0500    0.4500    0.7500
    0.2500    0.3500    0.0500    0.1500
    0.1500    0.9500    0.8500    0.4500
    0.8500    0.5500    0.9500    0.0500
    0.5500    0.2500    0.5500    0.3500
    0.3500    0.4500    0.7500    0.8500
    0.4500    0.1500    0.6500    0.6500
    0.0500    0.6500    0.2500    0.5500
    0.9500    0.8500    0.3500    0.9500
    0.7500    0.7500    0.1500    0.2500

y = corr(X);
(sum(y(:).^2) - 4)/2

ans = 0.0102

Minimizing the correlations results in a design with much lower sum of squared correlations.

Specify fewer iterations to improve the criterion.

rng default % For reproducibility
X = lhsdesign(10,4,'Criterion','correlation','Iterations',2)

X = 10×4

    0.6500    0.0500    0.4500    0.7500
    0.3500    0.3500    0.0500    0.1500
    0.1500    0.9500    0.8500    0.4500
    0.9500    0.5500    0.9500    0.0500
    0.5500    0.2500    0.5500    0.3500
    0.2500    0.4500    0.7500    0.8500
    0.4500    0.1500    0.6500    0.6500
    0.0500    0.6500    0.2500    0.5500
    0.8500    0.8500    0.3500    0.9500
    0.7500    0.7500    0.1500    0.2500

y = corr(X);
(sum(y(:).^2) - 4)/2

ans = 0.0328

Lowering the number of iterations results in a worse design (higher sum of squared correlations).

Input Arguments

n — Number of returned samples

positive integer

Number of returned samples, specified as a positive integer.

Example: 24

Data Types: single | double

p — Number of returned variables

positive integer

Number of returned variables, specified as a positive integer.

Example: 4

Data Types: single | double

Name-Value Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: X = lhsdesign(n,p,'Smooth','off') returns a discrete Latin hypercube design

Smooth — Indication for continuous samples

'on' (default) | 'off'

Indication for continuous samples, specified as the comma-separated pair consisting of 'Smooth' and 'on' (continuous samples) or 'off' (discrete samples). When this option is 'off', the returned values in each column of X are a random permutation of the values 0.5/n, 1.5/n, …, 1 – 0.5/n.

Example: 'Smooth','off'

Data Types: char | string

Criterion — Criterion for iterative sample generation

'maximin' (default) | 'none' | 'correlation'

Criterion for iterative sample generation, specified as the comma-separated pair consisting of 'Criterion' and 'maximin', 'none', or 'correlation'. The algorithm uses up to Iterations tries to improve the criterion.

Note

The 'correlation' criterion gives discrete samples, as if Smooth is set to 'off'.

Criterion	Description
'maximin'	Maximize the minimum distance between points.
'correlation'	Minimize the sum of between-column squared correlations.
'none'	No iteration

Example: 'Criterion','correlation'

Data Types: char | string

Iterations — Maximum number of iterations to improve criterion

5 (default) | positive integer

Maximum number of iterations to improve Criterion, specified as the comma-separated pair consisting of 'Iterations' and a positive integer. The algorithm uses up to Iterations tries to improve the criterion.

Example: 'Iterations',10

Data Types: single | double