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Have you tried web searching on " R constrained linear regression" or similar. There seemed to be resources related to your issues when I looked. You might also search on rseek.org . There are apparently several packages that do regression with constraints, but I don't know if they fit your situation. Cheers, Bert Bert Gunter "The trouble with having an open mind is that

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The nls2 package can be used to get starting values. On Thu, Apr 28, 2016 at 8:42 AM, Aleksandrovic, Aljosa (Pfaeffikon) <Aljosa.Aleksandrovic at man.com> wrote: > Hi Gabor, > > Thanks a lot for your help! > > I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to

Ok, and if I would just like to force my slope coefficients to be inside an interval, let's say, between 0 and 1? Is there a way in R to formulate such a constraint regression? Thanks in advance and kind regards, Aljosa Aljosa Aleksandrovic, FRM, CAIA Quantitative Analyst - Convertibles aljosa.aleksandrovic at man.com Tel +41 55 417 7603 Man Investments (CH) AG Huobstrasse 3 | 8808

This is a quadratic programming problem that you can solve using either a quadratic programming solver with constraints or a general nonlinear solver with constraints. See https://cran.r-project.org/web/views/Optimization.html for more info on what is available. Here is an example using a nonlinear least squares solver and non-negative bound constraints. The constraint that the coefficients sum

If the slope coefficients sum to a constant, the regressors are dependent and so a unique solution is impossible (an infinity of solutions would result). So I think you have something going on that you don't understand and should consult a local statistician to help you formulate your problem appropriately. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people

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Hi Gabor, Thanks a lot for your help! I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to be inside an interval, let?s say, between 0 and 1, what kind of starting values are normally recommended for the start argument (e.g. Using a 4 factor model with b1, b2, b3 and b4, I tried

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Hi all, I hope you are doing well? I?m currently using the lm() function from the package stats to fit linear multifactor regressions. Unfortunately, I didn?t yet find a way to fit linear multifactor regressions with constraint coefficients? I would like the slope coefficients to be all inside an interval, let?s say, between 0 and 1. Further, if possible, the slope coefficients should add up to

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