similar to: Optimization - n dimension matrix

Hey, R Users my windows is 64 bit windows 7.?I am trying to install the package ucminf into my 64 bit version R but cannot.??the package I downloaded is from http://cran.r-project.org/web/packages/ucminf/index.html?and I installed it with the "install from local zip files", due to I did not connect my computer to internet. did anyone meet this problem and is there a version of

While working on 'random walk' applications, I got interested in optimizing noisy objective functions. As an (artificial) example, the following is the Rosenbrock function, where Gaussian noise of standard deviation `sd = 0.01` is added to the function value. fn <- function(x) (1+rnorm(1, sd=0.01)) * adagio::fnRosenbrock(x) To smooth out the noise, define another

I have not included the previous postings because they came out very strangely on my mail reader. However, the question concerned the choice of minimizer for the zeroinfl() function, which apparently allows any of the current 6 methods of optim() for this purpose. The original poster wanted to use Newton-Raphson. Newton-Raphson (or just Newton for simplicity) is commonly thought to be the

Ravi Varadhan and I have been looking at UCMINF to try to identify why it gives occasional (but not reproducible) errors, seemingly on Windows only. There is some suspicion that its use of DBLEPR for finessing the Fortran WRITE() statements may be to blame. While I can find DBLEPR in Venables and Ripley, it doesn't get much mention after about 2000 in the archives, though it is in the R FAQ

Ravi Varadhan and I have been looking at UCMINF to try to identify why it gives occasional (but not reproducible) errors, seemingly on Windows only. There is some suspicion that its use of DBLEPR for finessing the Fortran WRITE() statements may be to blame. While I can find DBLEPR in Venables and Ripley, it doesn't get much mention after about 2000 in the archives, though it is in the R FAQ

Hi, I'm having trouble using the constrOptim function to generate the 9-component vector argmin of the function ELfsds: ELfsds <- function(pvechat){ LG=0 for(i in 1:9){ LG=LG+log(pvechat[i]) } return(-LG) } with accompanying gradient function: gradfunc <- function(thetavec){ g=1/(9*thetavec) return(g) } The constraints on the optimization problem are: 1 - components of

Hi everyone, I'm involved in a discussion with a colleague. He suggested a sample design for a finite-sized process that (to all intents and purposes) involves tossing a coin and examining the unit if the coin shows Heads. I should emphasize that we're both approaching the problem from a design-based sampling theory point of view. So I have no argument about the appropriateness of the

Strange things are going on in R, if you reshape a matrix in R: > g=gretldata[1:2,] > g Empfang Versand Transit Inland Ausland SumS 1 787844.0 1307176.6 223395.4 1474726 16199.1 3809341 2 421473.1 306445.4 448801.2 1779402 14445.6 2970567 > dim(g) [1] 2 6 > as.vector(g) Empfang Versand Transit Inland Ausland SumS 1 787844.0 1307176.6 223395.4 1474726 16199.1

Hi all, I want to use "which.max" to identify the maximum in a 2D array/matrix and I want "argmin" and return the row and column indices. But "which.max" only works for vector... Is there any convinient way to solve this problem? Thanks a lot! [[alternative HTML version deleted]]

Hi all Just wondering whether one can undertake Lp norm estimation (a type of regression analysis) in R? i.e. argmin S ( | y(i) - x(i)b | ^p ) where: * S is the summation over observation i= 1,2,...,n * y is a vector of n observations * x is an n by p matrix of explanatory variables * b is a p by 1 vector of beta coefficients and * p is a constant to be estimated such that

This is part of my program. I am getting an error, that I cannot figure out, any help would very much appreciated, thanks. # subset variables arc <- arc[,c("SNAP", "code", "ncode", "var", "n_total")] Error in `[.data.frame`(arc, , c("SNAP", "code", "ncode", : undefined columns selected arc$N_eff <-

The R "cut" command is entirely different from the UNIX "cut" command. The latter retains selected fields in a line of text. I can do that kind of manipulation using sub() or gsub(), but it is tedious. I assume there is an R function that will do this, but I don't know its name. Can you tell me? I'm also guessing that there is a web page somewhere that will tell

Hi, How i can install the package "adapt" in some version of R for mac? i try in 2.13, 2.9,2.7 and other previous versions... and nothing happens. and another question: There are some packages that do the same but that it is implemented for mac? (calculate integrals in 2 or more dimmensions). help me please, it's for an important work. greetings. -- Matías Hernán Ramírez

Hello. I'm trying to use the functions pandit and treebase. They are in the package apTreeshape. Once I've loaded the package, R responses: - no function pandit/treebase. Somebody knows why or what is the reason? Thanks, Arnau. ------------------------------------------------------------ Arnau Mir Torres Edifici A. Turmeda Campus UIB Ctra. Valldemossa, km. 7,5 07122 Palma de Mca.

I am wondering how to obtain the model/equation at each level automatically in a regression model with a few factors without looking at summary of the lm model. For example, consider lm.factors <- lm(y ~ x1 + factor(x2)*factor(x3)+x4*factor(x5)) The coefficients of lm.factors in summary(lm.factors) might be complicated. I would like to have the equation at each level from lm.factor. Could you

Hi, I have three matrices (X,Y,P) with the same dimension. The X,Y grid is regular and I want to perform linear interpolation to pick out certain points. In matlab appropriate call is something like Pout=interp2(X,Y,P,Xout,Yout, method="linear") where Xout and Yout are the locations where I want the Pout data (typically a different grid). (Scipy has this routine in

I'm fitting a regression model for censored data with three categorical predictors, say A, B, C. My final model based on the survreg function is Surv(..) ~ A*(B+C). I know the three-way group mean estimates can be computed using the predict function. But is there any way to obtain two-way group mean estimates, say estimated group mean for (A1, B1)-group? The sample group means don't

Dear community, I uploaded an excel with read.xls. My xls file actually have a column which is an id, ("plot" is the id) : plot height area 34 7.6 5.4 85 3.2 4.1 89 5.4 8.4 121 6.7 6.2 ... 1325 2.1 1.5 However R uses another id, this way: r id plot height area 1 34 7.6 5.4 2 85 3.2 4.1 3 89 5.4 8.4 4 121

I have a vector with lots of different numbers. I need to make a graph showing the Uniform Distribution of the figures. I have created a graph showing all the different values, but now want individual Gaussian Kernel round each point. This is what I have but each time it comes up with an error as I have just based it on the Normal Distribution, but I'm not sure what I need to change to make it

I'm trying to find the variance of various outputs in a matrix: for(l in 2:vl){ for(o in 1:(l-1)){ # Make sure the inputs are for the matrix "m" input3=rownames(v)[o] input4=colnames(v)[l] r=t[(t$Rec1==input3 & t$Rec2==input4),output] if(length(r)==0){ r=t[(t$Rec1==input4 & t$Rec2==input3),output] } v[l,o]=var(q,na.rm=TRUE)