Hi,
To control for covariates like age and gender in a multiple‐linear regression, you simply include them as columns in your design matrix (for regress) or as terms in your model formula (for fitglm).
To use "regress" :
% Suppose you have:
% Y : n×1 vector of your primary response
% age : n×1 vector of age
% gender : n×1 vector coded 0/1
% Xbio : n×p matrix of your p predictors of interest
n = 100;
Y = randn(n,1) * 10 + 50;
Age = randi([20, 70], n, 1);
Gender = randi([0, 1], n, 1);
Bio1 = randn(n,1);
Bio2 = randn(n,1);
% Design matrix (intercept + age + gender + biomarkers)
X = [ones(n,1), Age, Gender, Bio1, Bio2];
% Multiple linear regression
[beta, betaCI, residuals, ~, stats] = regress(Y, X);
% beta : (p+3)×1 vector of coefficients
% betaCI : 95% confidence intervals for each coefficient
% residuals : n×1 vector of residuals
% stats : [R^2, F-stat p-value, estimate of error variance, ...]
To use "fitglm":
T = table(Y, Age, Gender, Bio1, Bio2);
% Convert Gender to categorical
T.Gender = categorical(T.Gender, [0 1], {'Female','Male'});
% Linear regression
mdl = fitglm(T, 'Y ~ Age + Gender + Bio1 + Bio2');
disp(mdl)
disp('R-squared:'), disp(mdl.Rsquared.Ordinary)
disp('Coefficient table:'), disp(mdl.Coefficients)
Refer to these documentation links for more information on these functions:
Hope this helps!
