similar to: function in nls argument

Dear all, I am trying to use the C code for "integrate" function ( that calls Rdqagi and Rdqags) so that I can integrate a function defined in C, instead of passing from R. Is there a way for doing this? My unsuccessful attempt: I looked into the files (including integrate.c, Applic.h) and 1. modified the definition of "integr_fn" by droping the environment

Does anyone has a clue what went wrong in the following attempt? I am trying to call the R built-in function Rdqags() from my C program for numerical integration. Following are the C program and the corresponding R program: C program --------- void test(double *a, double *b, double *epsabs, double *epsrel, double *result,

Here is the code, assuming 8 cores in the cpu. library('modeest') library('snow') cl = makeCluster(rep('localhost', 8), 'SOCK') x = vector(length=50) x = sapply(x, function(i) i=sample(c(1,0), 1)) pastK = function(n, x, k) { if (n>k) { return(x[(n-k):(n-1)]) } else {return(NA)} } predR = function(x, k) { pastList = lapply(1:length(x), function(n)

Like kile for LaTeX, Linux/KDE's kate editor is an excellent editor for R, with easy code submission to a running R process. Syntax highlighting is good. I have not been able to figure out two things: - how to automatically reformat a line or region of text using good indentation rules (Emacs/ESS make this so easy by just hitting Tab while the cursor is in a line, or highlighting a

Dear R-help I'm working on a large dataset which I have divided into 20 subsets based on similar features. Each subset consists of observations from different locations and I wish to use the location as a random effect. For each group I want to select regressors by a stepwise procedure and include a random effect on the intercept. I use stepAIC() and lme(). (The lmer()-function doesn't

Dear R-help I'm working on a large dataset which I have divided into 20 subsets based on similar features. Each subset consists of observations from different locations and I wish to use the location as a random effect. For each group I want to select regressors by a stepwise procedure and include a random effect on the intercept. I use stepAIC() and lme(). (The lmer()-function doesn't

When I pass an empty string for cpu and features to createTargetMachine, and then use LLVMGetTargetMachineCPU() and LLVMGetTargetMachineFeatureString() to get the strings back, they are still empty. Is there a way to have llvm compute the effective cpu/features string, and provide it so that I can inspect it? I'm trying to figure out how the cpu/features string that I am explicitly passing,

Hi, A. In a nutshell: The training error, obtained as "error (ret)", from the return value of a ksvm () call for a eps-svr model is (likely) being computed wrongly. "nu-svr" and "eps-bsvr" suffer from this as well. I am attaching three files: (1) ksvm.R from the the kernlab package, un-edited, (2) ksvm_eps-svr.txt: (for easier reading) containing only eps-svr