similar to: Save parameters from optim during iteration procedure

Dear R-users, I am working with some grid-search optimization for 13 values of an object function. At glance one may compute the object function for each possible combination of set of parameters, but in my case would not be feasible: taking for example 13 possible values for each parameters like, in logarithm scale, 10^seq(-3,3,.5) will lead to 13^13 combinations of results. As a second trial

Dear R-users, within the rgl-package, I would have a question about the usage of persp3d in combination of rgl.viewpoint. I am not able to figure out how to let a 3D plot rotating around likewise the example in ?rgl.viewpoint. It seems that when I use persp3d(...) I see something on my screen, which is different from what I get when it's rotating. Is there any different behavior between

Dear R-users, I would like to place two 3D plots side-by-side in a rgl-setting. It would nice to have something like "par(mfrow=c(1,2))" for basic plots, or an array framework for wireframe(lattice) (see example below). I only managed to overlap two persp3d plots. My final idea would be to animate both surfaces using play3d(rgl). Thanks in advance for any help. Best, Carlo Giovanni

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Dear R-friends, I use optim(par=c(mystartingpoints), fn=myloglikelihoodfunction, gr=NULL, method=c("L-BFGS-B"), ## I would like to do not use any bounds control=list(trace=6, ## just to see what it's going on maxit=c(20000)), ## to be sure the it doesn't stop reaching the max iterations

Hello, I'd like to draw a graphic using different color line segments to represent microarray data. It simply likes this: Data=cbind(c(1, 2, -1, 3, -2.2), c(1, 3, -1, -1.8, 4)) The first col No represents y'axis position (negative down and positive up). The second col No represents a length of segment (negative segment should be on left of y, and positive on right). Also the

I am using R 2.2.1 on a Windows 2000 PC. When I do a grid() after the boxplot it overprints the boxplot: > boxplot(count ~ spray, data = InsectSprays, col = "lightgray") > grid(nx=NA, ny=NULL) > if I try the panel.first > boxplot(count ~ spray, data = InsectSprays, col = "lightgray", + panel.first=grid(nx=NA, ny=NULL)) > I can see the grid flash

Dear R-users, I'm struggling in R in order to "squeeze" a matrix without using a for-loop. Although my case is a bit more complex, the following example should help you to understand what I would like to do, but without the slow for-loop. Thanks in advance, Carlo Giovanni Camarda A <- matrix(1:54, ncol=6) # my original matrix A.new <- matrix(nrow=3, ncol=6) # a new

Dear R-users, I would like to know whether there is a way to extract a diagonal of an inverse matrix without computing the inverse of the matrix itself. The size of my matrices are really huge and, also using sparse matrix, computing the inverse leads to storage problems and low speed. In other words, given a square matrix A, I aim to know diag(B), where B=solve(A), without computing solve(A).

Dear R-user, I would like to apply spatial statistics on some historical data, in particular from 1850 to 1900 by registration district of England and Wales. I have searched in the R-archive, but unsuccessfully. Do you know whether R contains those shape files? Or where would be possible to download and use in R? Thanks in advance, Carlo Giovanni Camarda +++++ This mail has been sent through the

Dear R-users, would it be a better way to construct the matrix below without using any for-loop or model.matrix? preferably with some matrix algebra? Thanks in advance, Carlo Giovanni Camarda ## dimensions m <- 3 n <- 4 mn <- m*n k <- m+n-1 ## with a for-loop X <- matrix(0, mn, k) for(i in 1:n){ wr <- 1:m+(i-1)*m wc <- rev(1:m+(i-1)) where <- cbind(wr,wc)

Dear R-users, I would a small problem saving a graph with postscript after using xyplot. Whereas in the R-screen the two lines are solid, once the image is saved as .eps file the second lines (blue in the following example) is dotted. Here there is a simple example. Thanks in advance for your help, Carlo Giovanni Camarda # creating data a1 <- sort(runif(10)) a2 <- sort(runif(10)) a3 <-

Dear R-users, I would like to ask whether it's possible (for sure it would be), to plot each rows (or columns) in different graphs and in the same figure region without using the function "par" and then struggling around with "axes" and labels etc. Luckily, I would always have "rows + columns = even number" and the same "ylim". The next one could be a

Dear all, I would like to produce a map with information about the patenting activity in German districts, by coloring districts with different degrees of patenting activity in different colors. I work with the packages maptools, maps and spdep. The map data is read from an external .shp file (+ the corresponding .shx and .dbf files). Plotting a map with the IDs or the patenting indicator itself

Dear R-users, I am searching to the "best" way to compute a series of n matrix multiplications between each matrix (mXm) in an array (mXmXn), and each column of a matrix (mXn). Please find below an example with four possible solutions. The first is a simple for-loop which one might avoid; the second solution employs the tensor product but then manually selects the right outcomes. The

Dear R-Users, without going into details I tried to prepare a simple example to show you where I would need help. In particular I prepare two examples-template for a study I'm conduction on discrete-time methods for survival analysis. Each of this example has two datasets which are basically equal, with the exception that in the former one has individual data and in the latter one aggregated

Dear R-users, I couldn't find a function or some help in R-project web about the Generalized Singular Value Decomposition. In MatLab there is a simple function for this algebric issue (gsvd). Is there anything like that in R? And, if not, could you help me to apply this method in R? Thanks in advance, Giancarlo +++++ This mail has been sent through the MPI for Demographic Rese...{{dropped}}

Dear R-users, I would like to show you a simple example that gives an overview of one of my current issue. Although my working setting implies a different parametric model (which cannot be framed in the glm), I guess that what I'll get from the following example it would help for the next steps. Anyway here it is. Firstly I simulated from a series of exposures, a series of deaths (given a

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