similar to: Running Amelia with parallel processors in Windows

I know this is not a revolution support forum, but as anyone noticed the following? I have a foreach loop to generate random samples. If I run the exact code below in normal r (2.14.1) it works as expected, but if I run it from revolution 4.2.0 each loop returns the same numbers. The only way I can get revolution to give different numbers is using 1 instead of 8 in registerDoSNOW(makeCluster(8,

Hello I do not think I have fully grasped how to use Amelia to deal with missing data. For instance, suppose I have a data.frame variable with 4 columns (year, mon, ssn, dev) = (year, month, measurements, standard deviation of the measurement). Of course, there are some random missing values on columns 3 and 4. The measurements are an almost periodic time-series contaminated by noise.

Colleagues, I have started working with Amelia, with the aim of imputing missing data for time-series data. Although I have succeeded in getting Amelia to perform the imputation, I have not found any documentation describing how Amelia imputes time-series data. I have read the basic Amelia documentation, but it does not address how time-series data are imputed. The documentation describes

Dear all. First of all, my english isn't verry good, but I hope I can convey my concern. I've a general question about the Amelia algorithm. I'm no mathematician or statistician, but I had to use R and impute and analyse some data, and Amelia showed results that fitted my expectations. I'll have to defend my choice soon, but I haven't totally grasped what Amelia does. I'm

Hi everybody, I am quite new to data imputation, but I would like to use the R package ' Amelia II: A Program for Missing Data '. However, its unclear to me how the input for amelia should look like: I have a data frame consisting of numerous coulmns, which represent different experimental conditions, whereby each column has 3 replicates. I want amelia to perform an imputation across

Hi I have used the amelia command from the Amelia R package. this gives me a number of imputed datasets. This may be a silly question, but i am not a statistician, but I am not sure how to combine these results to obtain the imputed dataset to usse for further statistical analysis. I have looked through the amelia and zelig manuals but still can not find the answer. This maybe because I dont

I'm using bigkmeans in 'biganalytics' to cluster my 60,000 by 600,000 matrix. I'm using a 8 core Linux VM. I have register parallel backend with >registerDoMC() And I checked how many cores registered with >getDoParWorkers() It returns 8, which is the number of cores I have on my machine. And I run the test below, whose results shows improved speed due to parallel. check

[Apologies for the slightly-off-topic posting.] BizConf is a one-of-a-kind event that I am hosting about 3 months from now here in northern Florida. It''s a 2-day conference specifically crafted for independent consultants and owners/managers of small to mid-sized web consultancies. Because of who I am and the audience I can reach, I expect the majority of the attendees to be involved

Speaking of optimization and speeding up R calculations... I mentioned last week I want to speed up calculation of generalized inverses. On Debian Wheezy with R-2.15.2, I see a huge speedup using a souped up generalized inverse algorithm published by V. N. Katsikis, D. Pappas, Fast computing of theMoore-Penrose inverse matrix, Electronic Journal of Linear Algebra, 17(2008), 637-650. I was so

Due to missing data with some of my predictor variables I first do multiple imputation as follows: library(foreign) library(Amelia) library(norm) set.seed(666) M=10 impdat <-

I am trying to do parallel programming and I tried this library(doSNOW) library(foreach) testfunc<-function(x){ x<-x+1 x } noc<-2 cl <- makeCluster(do.call(rbind,rep(list("localhost"),noc)), type = "SOCK") registerDoSNOW(cl) clusterExport(cl=cl,c("testfunc.r")) testl<-foreach(pp=1:2) %dopar% { testfunc(pp) } And this works but if I try to

I am fairly new at using R/programming in general so I apologize if I am leaving crucial parts of the puzzle out, but here goes. First and foremost this is the error I am receiving: Error in muPriors[priors[, 1:2]] <- priors[, 3] : NAs are not allowed in subscripted assignments This occurs only when I am using observational priors and some number of nominal variables, it does not

Hello R-sig-debian and (hopefully) Dirk: On Debian wheezy, I have the R packaging that CRAN (you) provide. I run into a little trouble while trying to fiddle with alternative BLAS. I know you and I went around on this last year and I think perhaps I've found something wrong in the framework, or I've just done something wrong. I installed the packages openblas-base and openblas-dev, and

i have time series as 1.3578511 0.5119648 1.3189847 0.9214787 1.2272616 4.9167998 1.2272616 1.2272616 0.8854192 2.3386331 1.6132899 0.2030302 0.8426226 1.2277843 NA 1.3189847 1.3578511 0.8530141 2.3386331 1.0541099 0.7747481 0.5764672 1.3189847 1.2160533 1.2272616 0.6715839 0.9651803 1.6132899 1.2006974 0.6875047 1.3245534 1.2006974 0.8221709 1.3101684 1.6132899 1.6132899 1.2006974 1.3189847

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

Shouldn't -n be 4 in the bsub command? One master+3 slaves. This was required for snowfall, but I think doSNOW is similar. Hope it helps mario On 16-Dec-10 23:09, Marius Hofert wrote: > Dear expeRts, > > I try to get a minimal example for parallel computing via "foreach" + "doSNOW" to run on a computer cluster (Brutus from

Hi all, Within a foreach loop with doSNOW, we cant call functions which come from the non-default package. We need to load(require/library) the package once more within the foreach loop. Anyone knows why would happen like this? Is it caused by the snow package and something happened when "snow" parallelize the job? Other than load the package once more with in the foreach loop, is

R-users & helpers: I am using Amelia, mitools and cmprsk to fit cumulative incidence curves to multiply imputed datasets. The error message that I get "Error in eval(expr, envir, enclos) : invalid 'envir' argument" occurs when I try to fit models to the 50 imputed datasets using the "with.imputationList" function of mitools. The problem seems to occur

Dear list, I'd like to use multiple imputations to try and save a somewhat badly mangled dataset (lousy data collection, worse than lousy monitoring, you know that drill... especially when I am consulted for the first time about one year *after* data collection). My dataset has 231 observations of 53 variables, of which only a very few has no missing data. Most variables have 5-10% of

Hi, I have the following problem- I want to access a list whose elements are imp1, imp2, imp3 etc I tried theusing the paste comand in a for loop see the last for loop below. But I keep calling it df but df = imp1 (for the first run). Any ideas on how I can access the elements of the list? Isaac require(Amelia) library(Amelia) data.use <- read.csv("multiplecarol.CSV", header=T)