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Hi all, Running simulations, I'm generating market response to 2 factors X&Y.. There is no closed form for the market response.. The results are store in a matrix Z(X <- seq(.02,.98,.02), Y <- seq(.01,.19,.01)).. For optmization purpose I need to approximate the values for any factor X in 0,02-0,98 and Y in 0,01-0,19 How can I do it ? For one factor : Xn-1 < x <= Xn

> From: Weiwei Shi > > it works. > thanks, > > but: (just curious) > why i tried previously and i got > > > is.vector(sample.size) > [1] TRUE Because a list is also a vector: > a <- c(list(1), list(2)) > a [[1]] [1] 1 [[2]] [1] 2 > is.vector(a) [1] TRUE > is.numeric(a) [1] FALSE Actually, the way I initialize a list of known length is by

Hi all I've a continuous variable and I want to test (graphically, plotting observed and theoretical distr, qqplot) whether it follows some formal distribution. (some thing close to Ricci document : Fitting distributions with R, Feb05). The distribution I want to fit is a truncated Gamma at 1 (the minimal value is 1), P(x)=Pgamma(rate,x)/(1-Pgamma(rate,x<1)) NB : changing the variable

Hi all, I've just installed R on my Mac & PC. The base demo run fine.. But I'm encountering several pb with some packages (installed using CRAN binaries using the menu) - Lattice: > demo(lattice,package='lattice') demo(lattice) ---- ~~~~~~~ Type <Return> to start : > require(grid) [1] TRUE > old.prompt <- grid.prompt(TRUE) > old.settings

Hi all, I'm trying to estimate a nested model (purchase decision, cloglog formula, & quantity bought given a purchase, truncated Poisson). Some of the parameters are mixed (6) and 4 are fixed for all the respondent. The simulated ML (500 simulations) method forwards highly correlated estimates. After some research, Hybrid Monte Carlo seems to be a good alternative to estimate the model. I

Hello useRs and helpRs, Some time ago, in a gallaxy far away (here is the thread : http://finzi.psych.upenn.edu/R/Rhelp02a/archive/46532.html ) we discussed about a graph gallery showing the power of R in that domain. I did some work around that, and there is a (pretty advanced) draft here : http://addictedtor.free.fr/graphiques/displayGallery.php For instance, there are some of my graphs,

First, many thanks to Simon Urbanek for his help. A simulation case (50x34x5x3 & 250 replications) each based on behavior of 2500 consumers takes - 2h20mins on a laptop (2 MHz, 1Go, WinXp) - 1h on a G5 bi-proc (2.5 Mhz, 2Go) A loop was used for the laptop, the same loop was transposed as function for the Mac Best regards Naji

Hi All, I am running R 2.2.0 on Mac OS 10.4.2, dual G5 processors with 8 Gig RAM. I am running a simulation with lsoda that requires ~378 s to complete one set of time intervals. I need to optimize the parameters, and so need to considerably speed up the simulation. I have tried to figure out how to change the appropriate memory allocation and have search R help and Introductory