similar to: Constrained regression

Hello R helpers, I am trying to do a linear OLS regression of y on two variables x1 and x2. I want to constrain the coefficients of x1 and x2 to sum up to 1. and therefore run a constrained OLS. Can anybody help with this? (I have seen some answers to similar questions but it was not clear to me what I need to do) - I have tried the lm function with offset but I must not have used it properly.

Hello everyone, I have 3 variables Y, X1 and X2. Each variables lies between 0 and 1. I want to do a constrained regression such that a>0 and (1-a) >0 for the model: Y = a*X1 + (1-a)*X2 I tried the help on the constrained regression in R but I concede that it was not helpful. Any help is greatly appreciated -- Thanks, Jim. [[alternative HTML version deleted]]

I am fittting a linner regression with constrained parameters, saying, all parameters are non-negative and sum up to 1. I have searched historical R-help and found that this can be done by solve.QP from the quadprog package. I need to assess the significance of the coefficient estimates, but there is no standard error of the coefficient estimates in the output. So I can not compute the p-value.

Hello everyone, I am trying to estimate the parameter b. I have Y and X1 which I know and they are both random. However, I also have X2 which I don't know and is also random. I want to estimat b from the model: Y = b*X1 + ( 1 - b ) * X2 Can anyone offer some suggestions. The values of Y and X1 are both pvalues so they are constrained in (0,1). -- Thanks, Jim. [[alternative HTML version

Hi everyone, I'm trying to perform a linear regression y = b1x1 + b2x2 + b3x3 + b4x4 + b5x5 while constraining the coefficients such that -3 <= bi <= 3, and the sum of bi =1. I've searched R-help and have found solutions for constrained regression using quadratic programming (solve.QP) where the coefficients are between 0 and 1 and sum to 1, but unfortunately do not understand

Hi, I would like to know if anyone has any idea of how to run an OLS with constraints? I need to contraint a coefficient estimate in the model equal to 1, and I am not sure how to include it into the OLS estimation... I was hoping to find something like "cnsreg" in STATA.. thank you Soyoko ______________________________________ Ms. Soyoko Umeno Graduate Research Assitant for the

Dear list members, I am trying to get information on how to fit a linear regression with constrained parameters. Specifically, I have 8 predictors , their coeffiecients should all be non-negative and add up to 1. I understand it is a quadratic programming problem but I have no experience in the subject. I searched the archives but the results were inconclusive. Could someone provide suggestions

I wonder whether R has methods for constrained fitting of linear models. I am trying fm<-lm(y~x+I(x^2), data=dat) which most of the time gives indeed the coefficients of an inverted parabola. I know in advance that it has to be an inverted parabola with the maximum constrained to positive (or zero) values of x. The help pages for lm do not contain any info on constrained fitting. Does anyone

Hi all, A question came up in a code review (https://reviews.llvm.org/D72820) about whether or not to allow fast-math flags to be applied to constrained floating point intrinsics (http://llvm.org/docs/LangRef.html#constrained-floating-point-intrinsics). This has come up several times before, but I don't think we've ever made a decision about it. By default, the optimizer assumes that

This is fundamentally different than the problems we’re trying to handle with the constrained FP intrinsics. The constrained FP intrinsics were necessary because LLVM IR otherwise assumes that FP instructions do not have side effects. In the case of integer divide-by-zero, the optimizer generally recognizes this as a possibility and avoids introducing it (through speculation, for instance). But if

Hi everyone, According to the current design an intrinsic call that depends on current floating point environment (for example, rounding mode) or change it (for example by raising FP exceptions) is represented by constrained intrinsics. The latter have attached metadata that provide info about current FP environment and have attribute `IntrInaccessibleMemOnly` that helps keeping order of

Hi: I am trying to implement interval arithmetic through llvm. I have a problem with the rounding mode with llvm.experimental.constrained.cos I have two pieces of codes: ; Function Attrs: norecurse nounwind readnone ssp uwtable define double @cosine_down(double returned) local_unnamed_addr #0 {   ; call the llvm intrinsic to perform downward cosine

Dear All I have a constrained optimisation problem, I want to maximise the following function t(weights) %*% CovarianceMatrix %*% weights for the weights, subject to constraints on each element within the weights & the weights vector summing to 1. i.e. weights = (x1, x2, x3), where x1 is within some given range (a +b, a - b). I have tried to do this using the optim function in R,

I am trying to automatically construct a distance function from a training set in order to use it to cluster another data set. The variables are nominal. One variable is a "class" variable having two values; it is kept separate from the others. I have a method which constructs a distance matrix for the levels of a nominal variable in the context of the other variables. I want to

Dear list, The task view on optimization does not reference a package for non linear constrained optimization problems. Stefan Theussl told me to look at the Rsolnp package, but unfortunately it is not very clear what method is R ported. (The authors ported the matlab code of Yinyu Ye http://www.stanford.edu/~yyye/ <http://www.stanford.edu/%7Eyyye/>) Currently I'm looking for an

Please could you help me to find a package to apply a depth-constrained cluster analysis on palaeoecological data (in order to zone subfossil diagram)? Thanks, Dr. Emmanuel GANDOUIN Université d'Aix-Marseille 3 Institut Méditerranéen d'Ecologie et de Paléoécologie UMR CNRS 6116 Europôle Méditerranéen de l'Arbois, Bat. Villemin, BP 80 13545 Aix-en-Provence cedex 4 04 42 90 84 40

---------- Forwarded message ---------- Date: Fri, 4 Feb 2000 01:58:44 +0000 (GMT) From: Thomas Lumley <thomas@biostat.washington.edu> To: r-bugs@biostat.ku.dk Subject: constrained optimiser doesn't obey constraints The constrained optimiser is calling the function with parameter values outside the constrained range In this example the box is from c(0,0,0,0) to c(1,1,.5,1). The

> -----Original Message----- > From: Cameron McInally <cameron.mcinally at nyu.edu> > Sent: Friday, June 14, 2019 4:02 PM > To: Eli Friedman <efriedma at quicinc.com>; LLVM Developers Mailing List <llvm- > dev at lists.llvm.org> > Cc: Craig Topper <craig.topper at gmail.com>; Kaylor, Andrew > <andrew.kaylor at intel.com> > Subject: [EXT]

On Fri, Jan 17, 2020 at 8:09 PM Finkel, Hal J. <hfinkel at anl.gov> wrote: > Andy, thanks for writing this up. A few thoughts: > > 1. The mental model that I have is that there is always an FP_CONTRACT pragma: there's some default (implicit) pragma at the beginning, and what it says (off/on/fast) is controlled by the command-line flags (or the driver's default if no flags

_______________________________________________________________________________________ Hi, I was just wondering if there was any available R code that could handle general constrained optimisation problems. At the moment I'm using nlminb and optim, both of which allow box constraints on the parameters, but ideally I'd like to be able to specify more general constraints on the solution