similar to: Linear Programming.

Dear all, I want to program my own models about Stochastic Frontier Analysis and Linear programming (Data Envelopment Analysis). In this context, is there anyone that may help me with some simple tutorials and scripts about these issues? Thanks a lot. -- Marcus Vinicius Pereira de Souza, Prof. [[alternative HTML version deleted]]

Dear useRs, I am trying to build my package (nonpareff) which deals with some models of data envelopment analysis. The building worked well, but checking complains when it tests examples. Zipped nonparaeff.Rcheck is attached. Following is the log. --------------------------------------------- arecibo:tmp arecibo$ R CMD build nonparaeff/ * checking for file 'nonparaeff/DESCRIPTION' ...

Dear R team! =20 I found in HTML help for function solveLP(linprog) a small mistake. It says in Description "Minimizes c'x, subject to A x >=3D b and x >=3D 0", but tests show that there should be A x <=3D b. =20 Best regards, =20 Ludek =20 =20 [[alternative HTML version deleted]]

Dear R users, I´m studying about Bayesian Statistics. In this context, please, anyone have some basic script of RJMCMC (Reversible Jump Markov chain Monte Carlo) in R or WinBUGS? My aim is to learn how to implement this methodology. Thanks a lot. Marcus Vinicius [[alternative HTML version deleted]]

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

Full_Name: Nuno Monteiro Version: 2.3.1 OS: Windows XP HE Submission from: (NULL) (84.9.38.207) I'm using the FEAR library to perform Data Envelopment analysis with a 36,000 obs dataset. The function dea is working fine but then when I try to use the boot.sw98 to come up with some sensitivity analysis I get the following error:

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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:

Full_Name: wfeng Version: 2.7 OS: windows xp Submission from: (NULL) (208.62.252.2) for solveLP(linprog), the program is specified as Minimizes c'x, subject to A x >= b and x >= 0. However, what I found is the actual constraints that works with the function are A x <= b and x >= 0.

Dear useRs, I am trying to build my package (nonpareff) which deals with some models of data envelopment analysis. The building worked well, but checking complains when it tests examples. Zipped nonparaeff.Rcheck is attached. Following is the log. --------------------------------------------- arecibo:tmp arecibo$ R CMD build nonparaeff/ * checking for file 'nonparaeff/DESCRIPTION' ...

> > Dear R user´s, Is there anyone that may send me *Spatial Statistics* e-book from *Brian Ripley* ? Thanks a lot. Best regards. -- Marcus Vinicius P. de Souza Juiz de Fora - Minas Gerais Brasil [[alternative HTML version deleted]]

hi Using a ts or tprs basis, I expected gcv to decrease when increasing the basis dimension, as I thought this would minimise gcv over a larger subspace. But gcv increased. Here's an example. thanks for any comments. greg #simulate some data set.seed(0) x1<-runif(500) x2<-rnorm(500) x3<-rpois(500,3) d<-runif(500) linp<--1+x1+0.5*x2+0.3*exp(-2*d)*sin(10*d)*x3

Hola a todos, Simplemente comentar que me tengo encontrado con muchos problemas de optimización. Mi recomendación general, en el caso multidimensional y si el tiempo de computación es importante, sería buscar un algoritmo diseñado para el tipo de problema (evitar los algoritmos más generales tipo optim si puede haber problemas de mínimos locales). Algunos casos que tengo resuelto con R

Sir, Which modules/package should I use to solve the following statistical models: 1. Multiple Objective Programming (MOP) 2. Data Envelopment Analysis (DEA) 3. Lab Experiments 4. Multiple Criteria Decision Making (MCDM) Regards, PS

System: R 2.3.1 on a Windows XP computer. I am validating several cancer prognostic models that have been published with a large independent dataset. Some of the models report a probability of survival at a specified timepoint, usually at 5 and 10 years. Others report only the linear predictor of the Cox model. I have used Harrell's c index for censored data (rcorr.cens) as a measure of

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Dear R developers, (Apologies about previous incomplete e-mail, I fat-fingered a keyboard shortcut)?? I recently upgraded to 3.2.3 and am getting mystifying errors with my in-development code (available at https://github.com/gertvv/gemtc/tree/feature/regression). Every few hundred LPs that I solve using the rcdd package will result in a SEGFAULT in the garbage collector (address 0x10, cause

This is a bug/misfeature report for R CMD check. The function tools:::.check_packages_used_in_tests Gives an apparently unintended error when checking the tests in the contributed package rcdd_1.1-3.tar.gz as found on CRAN. See the script below for details. The actual error reported is totally mysterious. * checking for unstated dependencies in tests ... NOTE Error in

Estimados Reailizo una pregunta general, casi desconociendo. Se me ocurrió explorar lo siguiente: hay ejemplos de programación lineal o utilizando la herramienta solver de excel, donde se realizan algunos cálculos, lo más sencillo de comprender y documentado (todos lados) es una cantidad de productos, un costo de compra, un costo de venta, una cantidad para invertir y ¿cuánto me conviene

Dear all is there any linear programming routine available for R? if not, can you suggest any alternatives? not need to be very powerful, I get only a samll problem to resolve. many thanks yong