Stepwise Regression and PCA

Hi wishes all of u well. i am working on a project where 7 input and 1 output. The data is in numerical form. i want to apply stepwise Regression and PCA. any one please give me example code that help me in this regards. i use stepwise(x,y) function but it can not work can u help me in this regards.(<mailto:[e-mail] [e-mail]>) please send me example code here??thanks all

Answer 1

[Aditya]

Hi Adnan,

Stepwise Regression:

Stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In MATLAB, you can use the stepwiselm function for this purpose. Here's how you can do it:

% Example data
X = rand(100, 7);  % 100 samples, 7 input features
y = rand(100, 1);  % 100 samples, 1 output

% Perform stepwise regression
mdl = stepwiselm(X, y, 'linear', 'Criterion', 'bic');

% Display model summary
disp(mdl);

Principal Component Analysis (PCA)

PCA can be used for dimensionality reduction before applying regression. Here's how you can perform PCA on your input data and then use the transformed data for regression:

% Perform PCA on the input data
[coeff, score, latent, ~, explained] = pca(X);

% Determine how many principal components to keep (e.g., 95% variance)
cumulativeVariance = cumsum(explained);
numComponents = find(cumulativeVariance >= 95, 1);

% Use the selected principal components
reducedX = score(:, 1:numComponents);

% Perform stepwise regression using the reduced data
mdlPCA = stepwiselm(reducedX, y, 'linear', 'Criterion', 'bic');

% Display model summary
disp(mdlPCA);