You can set the tolerance value and variance inflation factor (VIF) threshold for multicollinearity in stepwise regression by customizing the options for `stepwiselm` function in MATLAB.
Here's a sample code:
% Define the predictors and response variable
X = [x1, x2, x3, x4, x5];
Y = y;
% Define the options for stepwise regression
options = statset('Display','iter', 'TolFun', 0.01, 'TolTypeFun', 'rel', 'PEnter', 0.05, 'PRemove', 0.1);
% Run the stepwise regression with predefined multicollinearity threshold settings
mdl = stepwiselm(X, Y, 'Criterion', 'bic', 'Upper', 'linear', 'Lower', 'constant', 'PEnter', 0.05, 'PRemove', 0.1, 'Verbose', 1, 'Options', options);
% Check the multicollinearity
[vif, tolerance] = vif(mdl);
% Check for variable inclusion
included_vars = mdl.predictorNames(mdl.Coefficients.Estimate ~= 0);
In the above code, you can customize the tolerance level and VIF threshold by setting the `options.TolFun` and `options.TolTypeFun` parameters respectively.
Please note that these are just sample values and you may need to adjust these parameters based on the requirements of your specific application. Additionally, `vif` function is used to compute the variance inflation factors for the coefficients of the model and `tolerance` function is used to compute the tolerance value for the predictors in the model.
I hope this helps! Let me know if you have any further questions.
