Hi Weixi
It is my understanding that you would like to include nuisance variables in the logistic regression using "fitglm" function.
Nuisance variables can be controlled by including them as additional predictor variables in the model. Including nuisance variables helps to account for their potential impact on the response variable and can improve the accuracy of the logistic regression model.
Here is an example to include nuisance variables in logistic regression using "fitglm":
% Generate some example data
rng(1); % For reproducibility
X1 = randn(100, 1); % Predictor variable 1
X2 = randn(100, 1); % Predictor variable 2
NuisanceVar = randn(100, 1); % Nuisance variable
Y = randi([0, 1], 100, 1); % Binary response variable
% Create a table with predictor and response variables
T = table(X1, X2, NuisanceVar, Y, 'VariableNames', {'X1', 'X2', 'NuisanceVar', 'Y'});
% Specify the logistic regression model
formula = 'Y ~ X1 + X2 + NuisanceVar';
% Fit the logistic regression model
mdl = fitglm(T, formula, 'Distribution', 'binomial');
% Display the model summary
disp(mdl);
You can refer to the following MathWorks documentation link to know more about "fitglm" function:
Hope this helps!
