similar to: definition of meq at spg

Hi, You can use the projectLinear argument in BB::spg to optimize with linear equality/inequality constraints. Here is how you implement the constraint that all parameters sum to 1. require(BB) spg(par=p0, fn=myFn, project="projectLinear", projectArgs=list(A=matrix(1, 1, length(p0)), b=1, meq=1)) Hope this is helpful, Ravi [[alternative HTML version deleted]]

Dear R Help, I have two nested negative log-likelihood functions that I am optimizing with the spg function [BB package]. I would like to perform model selection on these two objective functions using AIC (and possibly anova() too). However, the spg() function returns a list and I need a fitted model object for AIC(), ICtab() [bbmle package], or anova(). How can I perform AIC-based model

i'm trying to use the BB package to minimize the sum of the squared deviations for 2 vectors. The only thing am having trouble with is defining the project constraint. I got the upper and lower bounds to work but i am not sure how to create a constraint that the sum of x must be 1. Any help would be greatly appreciated. -- View this message in context:

A long formula which is converted using as.character, looses its last part: ``diagonal = 1e-12)'' Shorter formula is ok though. Best, H??vard ************ Browse[2]> formula.str y ~ -1 + b1 + b2 + b3 + b4 + b5 + b6 + b7 + b8 + b9 + b10 + b11 + b12 + b13 + b14 + b15 + b16 + b17 + b18 + b19 + b20 + b21 + b22 + b23 + b24 + b25 + b26 + b27 + b28 + b29 + b30 + b31 + b32 +

Estimados Hay un problema, miren lo que sale en b$Edad, esos valores son correctos, pero luego realizo str$Edad, aparecen 24, 24, 24, 5 ?. , convierto a números con b$Edad <- as.numeric(b$Edad) y los valores son 24, 24, 25 ? (valores que no son reales). Es fácil, lo realice muchas veces pero ahora estoy confundido. ¿Alguna ayuda? Gracias > b$Edad [1] 5 5 5 0,5 0,5 5 5 2 9

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

Hi. Here are three successive examples of simple quadratic programming problems with the same structure. Each problem has 2*N variables, and should have a solution of the form (1/N,0,1/N,0,...,1/N,0). In these cases, N=4,5,6. As you will see, the N=4 and 6 cases give the expected solution, but the N=5 case breaks down. >cm8 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 1 0

I forgot to attach the problem data, 'quadprog.Rdata' file, in my prior email. I want to report a following error with quadprog. The solve.QP function finds a solution to the problem below that violates the last equality constraint. I tried to solve the same problem using ipop from kernlab package and get the solution in which all equality constraints are enforced. I also tried an old

Dear R-users, I'm running a maximization problem in which I want to impose a condition on the relationship between 2 parameters. The condition is that w[4] = (1+eps)/(2*w[1]), or equivalently w[4]*w[1] = (1+eps)/2 , where eps is some small positive constant. I've been trying to formulate a function that takes care of this, but I can't really make it work so any suggestions would be

This patch (apologies for the size) updates zlib/* to the files that ship with zlib 1.1.3. Index: zlib/ChangeLog =================================================================== RCS file: /cvsroot/rsync/zlib/ChangeLog,v retrieving revision 1.1 diff -u -r1.1 ChangeLog --- zlib/ChangeLog 7 May 1998 06:19:41 -0000 1.1 +++ zlib/ChangeLog 30 Jan 2002 01:12:41 -0000 @@ -1,6 +1,54 @@ ChangeLog

Hi list: This is my first time to post my question on the list. Thanks for your help. I am solving a quadratic programming using R. Here is my question: w = arg min 0.5*w'Mw - w'N s. t. sum(w) = 1; w>0 note: w is weight vector, each w_i must >=0, and the sum of w =1. Here is my R code: A <-matrix(c(2.26,1.26,1.12,1.12,2.27,1.13,1.12,1.13,2.2),3,3); B <-

I want to use function cbind.na at library(qpcR) I install package qpcR and I can use functions such m1 <- pcrfit(reps, 1, 2, l5) > AICc(m1) [1] -102.5843 but when i try cbind.na(1, 1:7) i take message Error: could not find function "cbind.na" Thanks -- View this message in context: http://r.789695.n4.nabble.com/library-qpcR-cbind-na-tp4023339p4023339.html Sent from the

Is there an element-by-element multiplication in R, like the .* operator in Matlab? eg: A (2x3) B (2x3) C=A.*B C (2x3) C = [[a11*b11 a12*b12 a13*b13]; [a21*b21 a22*b22 a23*b23]] I can't find one... Thanks -Mike Gates

Hello, I propose two small changes to spec.pgram to get modest speedup when dealing with input (x) having multiple columns. With plot = FALSE, I commonly see ~10-20% speedup, for a two column input matrix and the speedup increases for more columns with a maximum close to 45%. In the function as it currently exists, only the upper right triangle of pgram is necessary and pgram is not returned by

Im trying to do regressions with constraints that the weights are all >=0 and sum(weights) = 1. I've read the archive and have set the problem up with solve.QP and just the non-negativity constraints along the lines of: y as the data vector X as the design matrix D <- t(X) %*% X d <- t(t(y) %*% X) A <- diag(ncol(X)) b <- rep(0,ncol(X)) fit <-

Dear R buddies, This weekend I became interested in solving Google Code Jam problems using R. I guess R may work very well in this kind of contests but the input of file has been a problem for me. Take this case for example (http://code.google.com/codejam/contest/dashboard?c=agdjb2RlamFtchALEghjb250ZXN0cxjRzBQM), the files are usually of the form: A(number of lines for group 1) a11 a12 a13 a21

Dear R-users I'm running a maximum likelihood procedure using the spg package. I'd like to save some output produced in each iteration to a file, but if I put the capture.output() within the function I get the following message; Error in spg(par = startval, fn = loglik, gr = NULL, method = 3, lower = lo, : Failure in initial function evaluation!Error in -fn(par, ...) : invalid argument

> > If one compares the random effect estimates, in fact, one sees that > > they are in the correct proportion, with the expected signs. They are > > just approximately eight orders of magnitude too small. Is this a bug? > > BLUPs are essentially shrinkage estimates, where shrinkage is > determined with magnitude of variance. Lower variance more > shrinkage towards

Hi everybody, I'm using Windows XP Prof, R 2.5.1 and a Pentium 4 Processor. Now, I want to solve a quadratic optimization program (Portfolio Selection) with the quadprog package I want to minimize (\omega'%*%\Sigma%*%\omega) Subject to (1) \iota' %*% \omega = 1 (full investment) (2) R'%*%\omega = \mu (predefined expectation value) (3) \omega \ge 0 (no short sales). Where

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in