Constraints on Parameter Estimation

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I am trying to fit linear regression model and predict parameters without intercept. I have written my code as under;
tbl=table(yobs,x1,x2,x3);
mdl = fitlm(tbl,'yobs ~ x1 + x2 + x3 - 1')
but I am getting the estimates which are negative but in my model all parameters should be positive. LB>=0 and UB=inf. How to set these constraints while doing the prediction.

Accepted Answer

Torsten
Torsten on 11 Mar 2023
Use lsqlin instead of fitlm.
  6 Comments
Torsten
Torsten on 13 Mar 2023
This is the best fit you can get without intercept and the constraints you want to impose on the parameters.
Torsten
Torsten on 13 Mar 2023
According to the documentation,
yobs ~ x1 + x2 + x3 - 1
means a three-variable linear model without intercept.
Thus the "-1" just means: no constant term, not
yobs = p1*x1 + p2*x2 + p3*x3 - 1
Very confusing.

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