similar to: maximizing available memory under windows XP

Hello All,   I¢m  kinda new to R language and any help that I can get is greatly appreciated.   Basically, I want to find the values of the two parameters that will maximize the function and I¢m currently using the optim function to find these values.  My R code works fine but not all the time.  Sometimes the solution converges, sometimes not.   Since I¢m planning to do this 5000 times, is there a

Dear List, I am trying to sub-sample some data by taking a data point every x minutes. The data contains missing values, and I would like to take the sub-sample that maximizes the number of valid points in the sample. I.e. minimizes the number of NA's in the data set. For example, given the following: da<-seq(Sys.time(),by=1,length.out=10) x<-c(1,2,NA,4,NA,6,NA,8,9,10)

Hi list, I have a set of 300 or so WAV files I was combining and playing using playback/background in 1.4.X. Now that I have moved on to the 10.0 set, I understand that I can replace my 8 Khz mono files with virtually unlimited Khz mono files (still no stereo, but a quantum leap forward). I've played with this and get good throughputs using SLIN44 formats on SIP. The 2 questions

Salve, this is just to probe whether other fans of an unimplemented feature exist or what priority/difficulty compiz coders would assign to it. I constantly used ?maximize vertically? (and also ?maximize horizontally?) in Metacity when there wasn't enough space to see a portion of text in a terminal or another window. Assigned to <Super_R>Up/<Super_R>Right it was a powerful pair

Hi R users, I am trying to figure out if there is a package in R that can maximize likelihood function with EM algorithm. Right now, I have derived the log-likelihood function, which is a function of 9 indicator variables with 14 parameters. Is there a package that I can specify the log-likelihood function, along with all variables and parameters, and will generate estimates? Thanks for help!

Hi, R Users; I am trying to maximize a likelihood function which contains an integral. The integral contains the unknown parameter as well. I am trying to use the following code to do the maximization: ll<-function(b.vec){ b0<-b.vec[1] b1<-b.vec[2] b2<-b.vec[3] p<-1/(1+exp(-b0-b1*z1-b2*x2))

See http://mayin.nfshost.com/ajayshah/KB/R/documents/mle/mle.html -- Ajay Shah http://www.mayin.org/ajayshah ajayshah at mayin.org http://ajayshahblog.blogspot.com <*(:-? - wizard who doesn't know the answer.

Note: This is somewhat related to Wine as fullscreen window support for X11 does not exist, at present, in the base X11 code. Many folks have posted threads asking why this is not so. Bottom line: Without good xrandr support, there will NEVER be true fullscreen support nor the ability to open X11 sessions with sized windows without using the virtual desktop. This is sorely needed and

for learning purposes and also to help someone, i used roger peng's document to get the mle's of the gamma where the gamma is defined as f(y_i) = (1/gammafunction(shape)) * (scale^shape) * (y_i^(shape-1)) * exp(-scale*y_i) ( i'm defining the scale as lambda rather than 1/lambda. various books define it differently ). i found the likelihood to be n*shape*log(scale) +

Hello, I have a log likelihood function that I was able to optimize using mle2. I have two years of the data used to fit the function and I would like to fit both years simultaneously to test if the model parameter estimates differ between years, using likelihood ratio tests and AIC. Can anyone give advice on how to do this? My likelihood functions are long so I'll use the tadpole

I recently got a new macbook pro 13", 2.4 Ghz, running Mac OS X 10.6.3. I am having trouble maximizing the Wine Desktop window that opens when I try running applications. It could be possible that the Wine window can't be stretched or made any bigger, but I'm not sure. If I try to make the window bigger by dragging the bottom right corner, the window gets larger, but it just adds a

A bioligist colleague sent me the following data. x Y 3 1 7 5 14 8 24 0 (Yes, only four data points.) I don't know much about the application, but apparently there are good empirical reasons to use a quadratic model. The goal is to find the X value which maximizes the response Y, and to find a confidence interval for this X value. Finding the maximizing X value is pretty

Dear All, I would like some advice on creating R packages, passing by reference and oo R. I have created a package that is neither elegant nor extensible and rather cumbersome (it works). I would like to re write the code to make the package distributable (should it be of interest) and easy to maintain. The package is for Bayesian model determination via a reversible jump algorithm and has

hello! ------ i use 32bit.Linux(SuSe)Server, so i'm limited with 3.5Gb of memory i demonstrate, that there is times to times a problem with allocating of objects of large size, for example 0.state (no objects yet created) ------------------------------------ > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 162070 4.4 350000 9.4 350000

I was surprised to observe the following difference between 2.4.1 and 2.6.0 after a long overdue upgrade a few months ago of our departmental server. It wasn't a bug fix, but a subtle improvement. Here's the simplest example I could create. The size is excessive, on the order of the Netflix Competition data. The integer matrix is about 1.12 GB, and if coerced to numeric it is 2.24 GB.

Hello all. I have a large .txt file whose variables are fixed-columns, ie, variable V1 goes from columns 1 to 7, V2 from 8 to 23 etc. This is a 60GB file with 90 variables and 60 million observations. I'm working with a Pentium 4, 1GB RAM, Windows XP Pro. I tried the following code just to see if I could work with 2 variables but it seems not possible: R : Copyright 2005, The R Foundation

Hi, when doing as.double() on an object that is already a double, the object seems to be copied internally, doubling the memory requirement. See example below. Same for as.character() etc. Is this intended? Example: % R --vanilla > x <- double(1e7) > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 234019 6.3 467875 12.5 350000 9.4 Vcells 10103774 77.1

Hi. As shown in the simplified example below, I'm having trouble allocating memory for large vectors, even though it would appear that there is more than enough memory available. That is, even with a memory limit of 1500 MB, R 2.6.1 (Win) will allocate memory for a first vector of 285 MB, but not for a second vector of the same size. Forcing garbage collection does not seem

Dear useRs? I have the following problem! I have a function that calls one or more functions, depending on the input parameters. I am searching for the fastest way to select and execute the selected functions and return their results in a list. The number of possible functions is 10, however usually only 2 are selected (although sometimes more, even all). For examples, if I have function

I believe there is a memory leak in isoreg in the current version of R, as I believe the following shows > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 120405 3.3 350000 9.4 350000 9.4 Vcells 78639 0.6 786432 6.0 392463 3.0 > for(k in 1:100) { + + y <- runif(10000) + isoreg(x,y) + } > rm(x) > rm(y) > gc() used (Mb) gc