As far as I know, there is not any built-in function specifically for MCAR test (such as Little’s MCAR test) in MATLAB.
However, you can implement the test yourself by using various built-in functions and libraries in MATLAB for statistical and matrix operations.
Below is a basic approach to implement a simplified version of Little’s MCAR test in MATLAB (the actual computation may require more sophisticated handling when the pattern of missingness is complex).
function [chi2stat, df, p] = littles_mcar_test(X)
% Listwise deletion to handle missing data
X_complete = X(~any(isnan(X), 2), :);
% Observed covariance matrix and mean vector
S_obs = cov(X_complete, 'partialrows');
mu_obs = mean(X_complete, 'omitnan');
% Expected covariance matrix under MCAR
S_exp = cov(X, 'partialrows');
% Degrees of freedom
[n, m] = size(X);
df = m * (m - 1) / 2;
% Chi-square statistic
chi2stat = n * (trace(S_exp \ S_obs) - log(det(S_exp \ S_obs)) - m);
% P-value
p = 1 - chi2cdf(chi2stat, df);
end
Hope this answers your query.
