Main Content

barttest

Bartlett’s test

collapse all in page

Syntax

ndim = barttest(x,alpha)
[ndim,prob,chisquare] = barttest(x,alpha)

Description

example

ndim = barttest(x,alpha) returns the number of dimensions necessary to explain the nonrandom variation in the data matrix x at the alpha significance level.

example

[ndim,prob,chisquare] = barttest(x,alpha) also returns the significance values for the hypothesis tests prob, and the χ2 values associated with the tests chisquare.

Examples

collapse all

Open Live Script

Generate a 20-by-6 matrix of random numbers from a multivariate normal distribution with mean mu = [0 0] and covariance sigma = [1 0.99; 0.99 1].

rng default  % for reproducibility
mu = [0 0];
sigma = [1 0.99; 0.99 1];
X = mvnrnd(mu,sigma,20);  % columns 1 and 2
X(:,3:4) = mvnrnd(mu,sigma,20);  % columns 3 and 4
X(:,5:6) = mvnrnd(mu,sigma,20);  % columns 5 and 6

Determine the number of dimensions necessary to explain the nonrandom variation in data matrix X. Report the significance values for the hypothesis tests.

[ndim, prob] = barttest(X,0.05)
ndim = 3

prob = 5×1

    0.0000
    0.0000
    0.0000
    0.5148
    0.3370

The returned value of ndim indicates that three dimensions are necessary to explain the nonrandom variation in X.

Input Arguments

collapse all

Input data, specified as a matrix of scalar values.

Data Types: single | double

Significance level of the hypothesis test, specified as a scalar value in the range (0,1).

Example: 0.1

Data Types: single | double

Output Arguments

collapse all

Number of dimensions, returned as a positive integer value. The dimension is determined by a series of hypothesis tests. The test for ndim = 1 tests the hypothesis that the variances of the data values along each principal component are equal, the test for ndim = 2 tests the hypothesis that the variances along the second through last components are equal, and so on. The null hypothesis is that the number of dimensions is equal to the number of the largest unequal eigenvalues of the covariance matrix of x.

Significance value for the hypothesis tests, returned as a vector of scalar values in the range (0,1). Each element in prob corresponds to an element of chisquare.

Test statistics for each dimension’s hypothesis test, returned as a vector of scalar values.

Introduced before R2006a

Statistics and Machine Learning Toolbox Documentation

Support

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

Download ebook