similar to: Constrained Optimization in R (alabama)

Hello, This is my first post to the help request list, so I'm going to err on the side of giving too much information. I'm working on writing a simulation in which agents will make repeated production and exchange decisions with randomly chosen partners. The idea is, all agents can produce two goods which they want to consume, they choose a value t in [0,10] which sets their production

Dear List, I'm new in R. I'm trying to solve a simple constrained optimization problem. Essentially, let's say I have a matrix as in the object 'mm' inside the function below. My objective function should have a matrix of parameters, one parameter for each element 'mm' (4 in this case). The problem is to select the value of the parameters to maximize the function

Dear List, I have the code below, where I am using the constrained optimisation package, 'constrOptim.nl' to find the values of two values, b0 and b1. I have no problems when I enter further variable information DIRECTLY into the functions, fn, and heq. In this instance I require fn to have -0.0075 appended to it, and in the case of heq, h[1] has -0.2. library(alabama)

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Dear Helper, I used "constrOptim.nl" and got the value of par. The estimations looks good even if the number of iterations is only 16. But the values of hessian and convergence are both "NULL". I tested the objective function and gradient function by "optim" and didn't see any problem there. With these functions, "optim" gives the convergence value

Hi, I am testing code that is working successfully using constrOptim. I am replacing constrOptim with other solvers to compare benefits and performance. My belief was that, like constrOptim, auglag in the alabama package would eliminate beta vectors that failed the constraints prior to calling the objective function. These betas are not only not in the solution space but also cause

Hello, I want to perform a nonlinear curve fit in order to obtain parameter estimates from experimentally determined data (y in dependence of x), but with an implicit function, thus, a function of which I cannot isolate y on the left-hand side of the equation. As far as I understand, the functions I found up to now (nls, optim) all work only for explicit functions. My data looks like

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

Hello everyone, I want to calculate type I error rate for modified F statistic for one way robust anova. I need to find the j group trimmed mean and winsorized sum of squared deviations. Here I attached my code for j=2 to make it simple. Originally I have j=4. Hope you can help. I need to run it for 1000 times My problem is: i) the value of F-test obtain from my simulation below is in negative

Dear R-users, I'm estimating a two- dimensional state-space model using the FKF package. The resulting log likelihood function is maximized using auglag from the Alabama package. The procedure works well for a subset of my data, but if I try to use the entire data set I get the following error message. Error in eigen(a$hessian) : infinite or missing values in 'x' What's even

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:

Hi All, I'm planning to submit my first package to R, and although I read all the documentation, I'm not very clear on the following 2 items, from which I'd appreciate your guidance: 1)I understand it is suggested to use the R dev version to build the package. Which one specifically should I use to build a package on a Mac OS? How about package dependencies, which version should I

Dear List, The gbm package on Win 7 produces different results for the relative importance of input variables in R 32-bit relative to R 64-bit. Any idea why? Any idea which one is correct? Based on this example, it looks like the relative importance of 2 perfectly correlated predictors is "diluted" by half in 32-bit, whereas in 64-bit, one of these predictors gets all the importance

Hi All, I didn't have much success through my Google search in finding any active R-related projects to create a wrapper around TensorFlow in R. Anyone know if this is on the go? Thanks, Axel. [[alternative HTML version deleted]]

Hi All, I didn't have much success through my Google search in finding any active R-related projects to create a wrapper around TensorFlow in R. Anyone know if this is on the go? Thanks, Axel. [[alternative HTML version deleted]]

Dear R users, I know this issue came up in the list several times. I’m currently running R on 32-bit on Windows and due to memory limitation problems would like to move to a 64-bit environment. I’m exploring my options and would appreciate your expertise: 1) Windows 64-bit: Prof. Brian Ripley recently posted the experimental built of R for win 64-bit. I’ll appreciate any feedback on

_______________________________________________________________________________________ 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

Hi All, I'm trying to do the following constrained optimization example. Maximize x1*(1-x1) + x2*(1-x2) + x3*(1-x3) s.t. x1 + x2 + x3 = 1 x1 >= 0 and x1 <= 1 x2 >= 0 and x2 <= 1 x3 >= 0 and x3 <= 1 which are the constraints. I'm expecting the answer x1=x2=x3 = 1/3. I tried the "constrOptim" function in R and I'm running into some issues. I first start off

Hi, I'm looking to move from Windows into a 64-bit Linux environment. Which is the best Linux Flavor to use within R? To install R on this environment, do I need to do any compiling? Thanks all! Axel. [[alternative HTML version deleted]]

Dear List, I'm trying to add a function that calls a Shiny App in my R package. The issue is that within my function, I'm creating objects that I'd like to pass to the app. For instance, from the example below, I'm getting "Error: object 'xs' not found". How can I pass "xs" explicitly to shinyApp()? *Under R directory:* myApp <- function(x, ...) {