similar to: Fix for nls bug???

Hi Everyone, I'd omitted the non-optional 'parameters' argument to selfStart. Making this change to SSbatch gives the same (successful) result from the two calls to nls. SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate coefficients as mean of each batch xy <- sortedXyData(mCall[["Batch"]],

Hi everyone, I thought that for a selfStart function, these two should be exactly equivalent > nls(Aform, DF) > nls(Aform, DF, start=getInitial(Aform, DF)) but in this example that is not the case in R (although it is in S-plus V6.2) ------------------------------ SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate

Hello! I'm doing a svar and when I make the estimation the next error message appears: In SVAR(x, Amat = amat, Bmat = bmat, start = NULL, max.iter = 1000, : The AB-model is just identified. No test possible. Could you help me to interpret it please. Also I have the identification assumption that one of my shocks is exogenous relative to the contemporaneous values of the other variables

Hi R-users, I would like to ask question related to error output. If an error comments come out, then the program will automatically stop. I want to ask , how I can still continue the program even though there is an error comment? var=VAR(Canada,p=3,type="const") for (j in 1:nrow(com)) { mat=ma { for (i in 1:ncol(com)) { y=which(mat==com[j,i]) mat[y]=NA }

This thread reveals that R has some holes in the solution of some of the linear algebra problems that may arise. It looks like Jim Ramsay used a quick and dirty approach to the generalized eigenproblem by using B^(-1) %*% A, which is usually not too successful due to issues with condition of B and making a symmetric/Hermitian problem unsymmetric. In short, the problem is stated as follows:

And remove the cast on the return value of R_GETCCallable. And check that your function is found before using it. #include <R.h> #include <Rinternals.h> #include <R_ext/Rdynload.h> void bdsmatrix_prod4(int nrow, int nblock, int *bsize, double *bmat, double *rmat, int nfrail, double *y) { DL_FUNC fun = NULL; if (fun==NULL)

I've recently updated the coxme package, which calls internal routines from the bdsmatrix package.? (It is in fact mentioned as an example of this in the Extensions manual.) The call connections are a blocks like this, one for each of the 9 called C routines. void bdsmatrix_prod4(int nrow,??? int nblock,?? int *bsize, ??????????????????? double *bmat, double *rmat, ??????????????????? int

Hello folks! I'm using R-Package {vars} and I'm trying to estimate an A-Model. I have serious problems regarding the restrictions. 1) My A-Matrix needs (!) to have the following form: # 1 NA NA NA # 0 1 NA NA # 0 0 1 NA # 0 0 0 1 That is done in R by: A_Matrix <- diag(4) # main diagonal = 4 restrictions A_Matrix [1, 2] <- NA # A_Matrix [1, 3] <- NA #

Dear all, I need to compute tensor product of B-spline defined over equi-spaced break-points. I wrote my own program (it works in a 2-dimensional setting) library(splines) # set the break-points Knots = seq(-1,1,length=10) # number of splines M = (length(Knots)-4)^2 # short cut to splineDesign function bspline = function(x) splineDesign(Knots,x,outer.ok = T) # bivariate tensor product of

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

At the risk of ridicule for my deficient linear algebra skills, I ask for help using the solve.QP function to do portfolio optimization. I am trying to following a textbook example and need help converting the problem into the format required by solve.QP. Below is my sample code if anyone is willing to go through it. This problem will not solve because it is not set up properly. I hope I

Hi I'm having problems getting the "factorized" argument in solve.QP (part of the quadprog library) to work as expected. The helpfile states that when the factorized argument is set to TRUE, then the function requires the inverse of a square-root factor of the Hessian instead of the Hessian itself. That is, when factorized=TRUE, the Dmat argument should be a matrix R^(-1), such

Hello, I have a big matrix of size (20,5) -bmat . I have to loop though the rows in the matrix and create DIFFERENT matrices each time I go through the loop. counts=c(4,6,10); p=1; for (i in 1:length(counts)) { smat=bmat[p:p+i-1,]; p=p+i; } The problem is smat overwrites itself each time inside the loop. I would like to have smat1, smat2, smat3 instead of a single vector smat.

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

Dear all Nobody responded to my previous post so far so I try with more offending subject. I just encountered a strange problem with nls formula. I tried to use nls in cycle but I was not successful. I traced the problem to some parse command. Here is an example DF<-data.frame(x=1:10, y=3*(1:10)^.5+rnorm(10)) coef(lm(log(DF[,2])~log(DF[,1]))) (Intercept) log(DF[, 1]) 0.7437320

Dear all, I need to maximize the v: v= D' W D D is a column vector ( n , 1) W is a given matrix (n, n) subject to: sum D= 1 (BTW, n is less than 300) I´ve tried to use maxBFGS, as follows: ##################################### objectiveFunction<-function(x) { return(t(D)%*%W%*%D) } Amat<-diag(nrow(D)) Amat<-rbind((rep(-1, nrow(D))), Amat) bvec<-matrix( c(0), nrow(D)+1,

I'm working on the next version of coxme, one step of which is converting the bdsmatrix library from Splus to R. Actually, it is a conversion from S4 methods as first described in the Green book to S4 methods as they currently exist. Mostly it's going ok, but not entirely. 1. The biggest issue is lack of documentation. The online help pages have not been a help; they keep saying

Hi, I'm using the package quadprog to solve the following quadratic programming problem. I want to minimize the function (b_1-b_2)^2+(b_3-b_4)^2 by the following constraints b_i, i=1,...,4: b_1+b_3=1 b_2+b_4=1 0.1<=b_1<=0.2 0.2<=b_2<=0.4 0.8<=b_3<=0.9 0.6<=b_4<=0.8 In my opinion the solution should be b_1=b_2=0.2 und b_3=b_4=0.8. Unfortunately R doesn't find

I'm having difficulty figuring out how to implement the following set of constraints in Quadprog: 1). x1+x2+x3+x4=a1 2). x1+x2+x5+x6=a2 3). x1+x3+x5+x7=a3 4). x1+x2=b1 5). x1+x3=b2 6). x1+x5=b3 for the problem: MIN (x1-c1)2+(x2-c2)2+...+(x8-c8)2. As far a I understand, "solve.QP(Dmat, dvec, Amat, bvec, meq=0, factorized=FALSE)" reads contraints using an element-by-element

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 <-