similar to: how to code a restriction matrix with the following restrictions

Dear R Users, I would like to make a lesast squares regression similar to that what is done by the command "lm". But additionally, I would like to impose some restrictions: 1) The sum of all regression coefficients should be equal to 1. 2) Each coefficient should assume a value between 0 and 1. (inequality restrictions) Which command is the best to use in order to solve this problem

Dear All, Which package/function could i use to solve following linear least square problem? A over determined system of linear equations is given. The nnls-function may would be a possibility BUT: The solving is constrained with a inequality that all unknowns are >= 0 and a equality that the sum of all unknowns is 1 The influence of the equations according to the solving process is

Hello, I am attempting to utilize the 'alabama' package to solve a constrained nonlinear optimization problem. The problem has both equality and inequality constraints (heq and hin functions are used). All constraints are smooth, i.e. I can differentiate easily to produce heq.jac and hin.jac functions. My initial solution is feasible; I am attempting to maximize a function, phi. As

Hi,, In src/lib/mountpoint.c:53 the following had to be commented out: // point_r->type = p_strdup(pool, buf.f_fstypename); because f_fstypename is undeclared on this OS. Also the following errors occur with the DEC C compiler (which compiles the other files just fine): source='file-cache.c' object='file-cache.o' libtool=no DEPDIR=.deps depmode=none

Hi everyone, i?m a new user of R and i?m trying to translate an linear optimization problem from Matlab into r. The matlab code is as follow: options = optimset('Diagnostics','on'); [x fval exitflag] = linprog(f,A,b,Aeq,beq,lb,ub,[],options); exitflag fval x=round(x); Where: f = Linear objective function vector (vector of 45,rows) A = Matrix for linear inequality

Hello, I've got to compute a minimization equation under an equality constraint (Min g(x1,x2,x3) with x1+x2=const). The Constroptim function does not authorize an equality condition but only inequality conditions. Which function can I use instead? Thank you very much for your help. Gael Robert - +33 1 42 14 27 96 ****************************************************************** This

Dear All, I would like to minimise a nonlinear function subject to linear inequality constraints as part of an R program. I have been using the constrOptim function. I have tried all of the methods that come with Optim, but nothing finds the correct solution. If I use the correct solution as the vector of starting values, though, my program does output the correct solution and optimum - the

If I use the Ineq library and the Gini function in this way: >Gini(c(100,0,0,0)) I obtain the result 0.75 instead of 1 (that is the perfect inequality). I think Gini's formula in Ineq is based on a formula as reported here: http://mathworld.wolfram.com/GiniCoefficient.html but in the case of perfect inequality: x_1=.......=x_n-1 =0 x_n>0 these formula are equal to 1 - 1/n, not to

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

Dear R help list, This is a long post so apologies in advance. I am estimating a model with the mgcv package, which has several covariates both linear and smooth terms. For 1 or 2 of these smooth terms, I "know" that the truth is monotonic and downward sloping. I am aware that a new package "scam" exists for this kind of thing, but I am in the unfortunate situation that I am

On Wed, Dec 12, 2012 at 2:45 PM, Joerg Sonnenberger <joerg at britannica.bec.de> wrote: > On Wed, Dec 12, 2012 at 01:59:55PM -0800, Dan Gohman wrote: >> On Wed, Dec 12, 2012 at 1:26 PM, Joerg Sonnenberger >> > The original issue is that clang maps restrict on function arguments to >> > NoAlias and that makes compares against the address of global variables >>

Is there a package in R that handles general linear (in-)equality + box constrained optimization? If it is not there, could anyone advise me which way to go? And/or point me to packages that solve these problems partially? best, ingmar -- Ingmar Visser Department of Psychology, University of Amsterdam Roetersstraat 15, 1018 WB Amsterdam The Netherlands http://users.fmg.uva.nl/ivisser/ tel:

Hello R users, Can anyone recommend me any package that can be used to solve linear programming subject to nonlinear equality/inequality constraints. Rdonlp2 is now unavailable due to licensing issue. Thanks, Xiaoxi [[alternative HTML version deleted]]

Dear list, I am trying to follow an example that estimates a 2x2 markov transition matrix across several periods from aggregate data using restricted least squares. I seem to be making headway using solve.QP(quadprog) as the unrestricted solution matches the example I am following, and I can specify simple equality and inequality constraints. However, I cannot correctly specify a constraint

Hello All, I would like to optimize a (log-)likelihood function subject to a number of linear constraints between parameters. These constraints are equality constraints of the form A%*%theta=c, ie (1,1) %*% 0.8,0.2)^t = 1 meaning that these parameters should sum to one. Moreover, there are bounds on the individual parameters, in most cases that I am considering parameters are bound between zero

Dear helpers, I am using the function "constrOptim" to estimate a model with ML with an inequality constraint using the option method='Nelder-Mead'. When I specify the option: hessian = TRUE I obtain the response: Error in f(theta, ...) : unused argument(s) (hessian = TRUE) I guess the function "constrOptim" does not allow this argument which, on the other hand, is

Hello, I was interested in trying to write an R script to calculate a UCL for a lognormal distribution using the Chebyshev Inequality — MVUE Approach (based on EPA’s guidance found in http://www.epa.gov/oswer/riskassessment/pdf/ucl.pdf). This looks like it should be straight forward, but I am need to calculate an MVUE for the population mean and an MVUE for the population variance, which requires

Dear UseRs, I wrote following function in order to solve Data Envelopment Analysis. Reason for posting is that the function is slow when nrow(dat) is large. I wonder if other functions could substitute the for() loop in the code, such as mapply(). Can anybody help to rewrite the dea() function as efficiently as possible? The code is as follows:

Hi, i need to minimize a quadratic function with boundary condidtions and one equality condition. In order to do that i converted the equality constraint into 2 inequality constaints and passed everything cia constrOptim, as the manual said: everything included in the ... will be passed to Optim that will pass it back to fn in case it does not need it. My code is the following: mat <-

Please advice on the package I should use to run a linear regression model (weighted least squared) with linear equality constraint. I initially tried "constrOptim" but it turned out that it only supported inequality linear constraint. Thank you very much in advance. Cheers, Jon -- View this message in context: