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.
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Torsten
2023-3-11
0 个投票
Use lsqlin instead of fitlm.
6 个评论
Faizan Lali
2023-3-13
C = [x1 x2 x3];
d = yobs;
lb = [0.5 0 0];
ub = [inf inf inf];
sol = lsqlin(C,d,[],[],[],[],lb,ub)
where x1, x2, x3 and yobs are column vectors of the same length doesn't work ?
Perhaps the columns of C need to be normalized.
C = [x1 x2 x3];
c=vecnorm(C,2,1);
d = yobs+1;
lb = [0.5 0 0];
ub = [inf inf inf];
sol = lsqlin(C./c,d,[],[],[],[],lb,ub);
sol=sol./c(:)
Faizan Lali
2023-3-13
Torsten
2023-3-13
This is the best fit you can get without intercept and the constraints you want to impose on the parameters.
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