similar to: scaling of parameter errors in a weighted regression using nls

Dear all, I've run into a question of handling large matrices in R. I'd like to define a 70000*70000 matrix in R on Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit), but it seems to run out of memory to handle this. Is it due to R memory limiting size or RAM of my laptop? If I use a cluster with larger RAM, will that be able to handle this large matrix in R? Thanks much! Best, Hui

Hi all, Lets say I have a matrix A which is m x n. I also have a mask matrix MASK which is m x n with values in T/F, where T values make a sub-matrix in regutangular shape... I applied B=A[MASK] and it didn't work as expected... Any thoughts? [[alternative HTML version deleted]]

Hello, (Sorry for the repost, i am resending in plain text) I have a function 'ewrap' (see below for definition). It takes 3 expressions and returns another expression e.g. map <- ewrap({ len <- length(r$addon) rhcollect(len,1) }) becomes: expression({ NULL result <- mapply(function(.index, k, r) { { len <- length(r$addon)

hello I want a code which can correct the spelling mistakes as well as grammatical mistakes in the sentences...that is if I am writing following sentence: I want too meet my frnd bt due to hectic shcedule I cant I want output in following way: I want to met my friend but due to hectic schedule I can't. Thanks in advance Shilpa Rai MSc IIT Bomaby -- View this message in context:

Hi, here i have some code... a <-1 b <- b+1 c <- b+a/20 if c >5 { d<- 1 } else { d<- 0 } i want to repeat this code from the 2nd line [b <- b+1] without using loop. Actulay i want to use "*Label *" and call *GoTo* method like in other coding Languages. For eg:- like this, a <-1 *LabelBegin :* b <-

I have photographs of plots that look like so: http://r.789695.n4.nabble.com/file/n4631960/Untitled.jpg I need to divide it up so each circle has an equal area surrounding it. So into 20 equal segments, each of which contains a circle. Quadratcount is not sufficient because if I divide it up into 36 equal quadrats, some quadrats do not contain one of the circles. I'm not even sure how to

Dear R Users: I'm a STATA user converting to R, and I'd like to be to do the following. #Assign var_1 and var_2 a value 10->var1 20->var2 #Now I'd like to print the values of var_1 and var_2 by looping through var_1 and var_2 in such a manner: while(y<3){ print(var_y) y+1->y } In STATA, the "y" appended to " var_" is called the local variable and

Dear All, I have been searching online for help increasing my R code more efficiently for almost a whole day, however, there is no solution to my case. So if anyone could give any clue to solve my problem, I would be very appreciate for you help. Thanks in advance. Here is my issue: My desktop is with i7-950 Quad-core CPU with 24Gb memory, and a NVIDIA GTX 480 graphic card, and I am using a

Hello, Is there a convenient way to import RDF/OWL data into R? I'm interested in importing BioPAX/SBPAX data into R to make them available for a wider audience. One exciting application would be to use pathway data to explain differential microarray measurements by identifying upstream nodes that are likely involved in causing the differences. This could also be used to validate

Hello everybody I want to use rls() on a multi-dimensional function, where parts of it are modeled using a spline. I tried to condense my problem into the following code example, which tries to fit the y-values of a spline interpolation: --------------------------------------------------------------------------- rm(list=ls(all=TRUE)) x<-c(1,2,3,4,5,6,7,8,9) y<-x^2

Full_Name: Tadashi Kadowaki Version: 2.4.0 OS: Redhat Linux 9 Submission from: (NULL) (58.12.166.67) Doesn't nls function support subset? It seems not to work. And, there are no information in the online help. Has it sunk into oblivion?

Dear Colleagues, I am trying to estimate several non-linear models simultaneously. I don't want to use non-linear mixed model, but non-linear model with same form, but it should be estimated separately according to variable group (I have lots of groups that have lots of observations....). I would like to have unique parameters for each group. e.g. something like this mod <- nls(y ~

Hi, I am using nls to fit a non linear function to some data but R keeps giving me "singular gradient matrix at initial parameter estimates" errors. For testing purposes I am doing this: ### R code ### x <- 0:140 y <- 200 / (1 + exp(17 - x)/2) * exp(-0.02*x) # creating 'perfect' samples with fitting model yeps <- y + rnorm(length(y), sd = 2) # adding noise # results

Dear All, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) Now for a weighted fit: fm2DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal),

Dear R Community, I am attempting to perform a weighted non-linear least squares fit. It has already been noted that the weights option is not yet implemented for the nls function, but no one seems to offer any suggestions for getting around this problem. I am still curious if a) anyone has code they have written which includes a weight options for nls, or b) if there is another model which

Dear WizaRds, Given the experimental data, csdata<-data.frame( time=c(0,1,3,9,20), conc=c(638.697,395.69,199.00,141.58,112.16) ) weighted nls is applied, wt.MM<- function(resp, time,A1,a1,A2,a2) { pred <- A1*exp(-a1*time)+A2*exp(-a2*time) (resp - pred) / sqrt(pred) } # cs.wt <- nls( ~ wt.MM(conc, time,A1,a1,A2,a2), data=csdata,

for unweighted fits using `nls' I compute confidence intervals for the fitted model function by using: #------------------- se.fit <- sqrt(apply(rr$m$gradient(), 1, function(x) sum(vcov(rr)*outer(x,x)))) luconf <- yfit + outer(se.fit, qnorm(c(probex, 1 - probex))) #------------------- where `rr' contains an `nls' object, `x' is the independent variable vector, `yfit'

dear list, my question concerns the use of `eval' in defining the model formula for `nls' (version 2.6.2.). consider the following simple example, where the same model and data are used to perform unweighted and weighted fits. I intentionally used very uneven weights to guarantee large differences in the results #================================CUT=========================== ln

2 days ago I asked this on r-help, but no luck... since this is actually a programming question, I post it here again: my question concerns the use of `eval' in defining the model formula for `nls' when performing weighted fits. (I use version 2.6.2., but according to NEWS there were no changes to `nls' in 2.7.0, so the problem is still present). in this scenario their

I've been playing and comparing frequency scaling between AMD and Intel CPUs yesterday and there seem to be great differences between AMD and Intel and some gotchas. This is all on CentOS 5.2 with latest Xen kernels (which are supposed to be powersaving-enabled since 5.2). AMD: It seems once I get the AMD CPU to use the ondemand governor it works very well and very efficiently. But this