similar to: Debian packaging and openblas related crash when profiling in R

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

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)

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.

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

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 <-

R-helpers: I am using R 2.5 on Windows XP, packages all up to date. I have run into an issue with the MIcombine function of the mitools package that I hoped some of you might be able to help with. I will work through a reproducible example to demonstrate the issue. First, make a dataset from the pbc dataset in the survival package --------------- # Make a dataset library(survival) d <-

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

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

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

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

two OutputsHello! I am Amelia from Auckland and work for a bank. I am new to R and I have started my venture with R just a couple of weeks back and this is my first mail to R-forum. I need following assistance Suppose my R code generates following outputs as > X [[1]] [1] 40 [[2]] [1] 80    160 [[3]] [1] 160   80  400 > Y [[1]] [1] 10 [[2]] [1] 10    30 [[3]] [1] 5  18  20

Dear Sir, Thanks for the guidance. Will check. And yes, at the end of each simulation, a large result is getting stored.? Regards Amelia On Wednesday, 6 April 2016 5:48 PM, jim holtman <jholtman at gmail.com> wrote: It is hard to tell from the information that you have provided.? Do you have a list of the sizes of all the objects that you have in memory?? Are you releasing large

Point of clarification: Packages other than those that are part of the "standard" r distro must first be "installed" from a package repository -- typically CRAN -- via the install.packages()* function before they can be accessed via the library() or require() function. Have you done this? See ?installed.packages if you are unsure. Also, R must know what libraries to look in

Een ingesloten tekst met niet-gespecificeerde tekenset is van het bericht gescrubt ... Naam: niet beschikbaar Url: https://stat.ethz.ch/pipermail/r-help/attachments/20061106/756382a8/attachment.pl

Hi all, after using Amelia II to create 10 imputed data sets I need to average them to have one unique data that includes the average for each cell of the variables imputed, in addition to the values for the variables not imputed. Such data has many variables (some numeric, other factors), and more than 20000 observations. I do not know how to average them out. Any help? Below I provide a small

I am trying to add a copyright disclaimer outside the plot borders of some images I have created. I can use mtext() to add the written portion, but I would like to have the Creative Commons license image (http://en.wikipedia.org/wiki/File:Cc.logo.circle.svg) before the text. I've found that I can plot a .png image inside the plot boundaries using rasterImage() but I can't figure out how to

It is hard to tell from the information that you have provided. Do you have a list of the sizes of all the objects that you have in memory? Are you releasing large objects at the end of each simulation run? Are you using 'gc' to garbage collect any memory after deallocating objects? Collect some additional information with a simple function like below: f_mem_stats <-

Hello all, if I have multiple imputed data sets, is there a command or function in R in any package you know of to combine those, I know one common MI approach is rubins rules, is there a way to do this using his rules or others? I know theres ways, like using Amelia from Gary King's website to create the imputed data sets, but how to make them into one or combine them for analysis.

Hi all, may i know is it possible to plot a graph by first letter? for example: Name: Age: Angel 20 Amelia 20 Bernard 19 Stephanie 20 Vanessa 22 Angeline 23 Camel 21 If I want to plot the name started with letter 'A' and their Angel,