MATLAB Answers

Which function should I use for generating the weighted least squares fit linear line for a given data?

3 views (last 30 days)
Vihaan Misra
Vihaan Misra on 4 Oct 2020
Commented: Sindar on 5 Oct 2020
I have a set of data which I generate using the following code:
x = linspace(-10, 10, 20);
slope = 1.5;
intercept = -1;
noiseAmplitude = 15;
y = slope .* x + intercept + noiseAmplitude * rand(1, length(x));
I want to generate a weighted linear least squares fit regression line for the above data points. I don't have the weights matrix so I can go with using the formula w(i) = 1/variance(i)^2 or any other default formula that a MATLAB function may use for generating the weights matrix. I don't know how to generate this matrix and make sure its diagonal and then fit a regression line in the data, is there a function in MATLAB which can help me achieve this?

  0 Comments

Sign in to comment.

Accepted Answer

Sindar
Sindar on 4 Oct 2020
Edited: Sindar on 5 Oct 2020
fitlm accepts weights as a vector, but doesn't come with any pre-designed ones
mdl = fitlm(x,y,'Weights',weight);
Ypred = predict(mdl,x);
plot(x,y,'k*',x,Ypred,'r')
determining variance values for your points is a separate question (my answer would be "huh? I don't think that's a sensible quantity," but if you ask clearly, others may have an answer)

  5 Comments

Show 2 older comments
Sindar
Sindar on 5 Oct 2020
(if the variance is defined from your underlying distribution, it looks like it should be constant since your noise doesn't depend on x)
Vihaan Misra
Vihaan Misra on 5 Oct 2020
Thanks a lot!! I have been trying to generate the weights matrix but can't seem to do so, I am very new to MATLAB so any input will be really helpful

Sign in to comment.

More Answers (0)

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!