similar to: Help with Amelia

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

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 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

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.

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

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

Hi list, I am wondering if there is a way to use EM algorithm to handle missing data and get a completed data set in R? I usually do it in SPSS because EM in SPSS kind of "fill in" the estimated value for the missing data, and then the completed dataset can be saved and used for further analysis. But I have not found a way to get the a completed data set like this in R or SAS. With

Hi list, I am wondering if there is a way to use EM algorithm to handle missing data and get a completed data set in R? I usually do it in SPSS because EM in SPSS kind of "fill in" the estimated value for the missing data, and then the completed dataset can be saved and used for further analysis. But I have not found a way to get the a completed data set like this in R or SAS. With

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, I want to impute a data set multiple times with Amelia, but the data set is large so it takes a long time. As a result, I'm trying to run the multiple imputation with parallel processors in Windows, but am having trouble. Here is a quick example: ###### library(foreach) library(doSNOW) registerDoSNOW(makeCluster(4, type = "SOCK")) getDoParWorkers() getDoParName()

Dear R-group, Could somebody recommend a package that can deal with a multinomial response variable (choice of breeding tactic in mice, which has four unordered levels), multiply-imputed data (generated using the Amelia package) and two non-nested random effects: individual identity (133 individuals made up to four choices each) and year (for which there are six levels and sample size varies

Hi, all, I'm running multiple imputation to handle missing data and I'm running into a problem. I can generate the MI data sets in both amelia and the mi package (they look fine), but I can't figure out how to get pooled results. The examples from the mi package, zelig, etc., all seem to go right to something like a regression, though all I want are the mean and SE for all the

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

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.

Hello: I am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey

I'm trying to use either mice or norm to perform multiple imputation to fill in some missing values in my data. The data has some missing values because of a chemical detection limit (so they are left censored). I'd like to use MI because I have several variables that are highly correlated. In SAS's proc MI, there is an option with which you can limit the imputed values that are

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

I'm looking for a package that has a start-of-the-art method of imputation of missing values in a data frame with both continuous and factor columns. I've found transcan() in 'Hmisc', which appears to be possibly suited to my needs, but I haven't been able to figure out how to get a new data frame with the imputed values replaced (I don't have Herrell's book). Any

Dear R users and developers, I have a dataset containing 34 variables measured in a survey, which has some missing items. I would like to conduct a factor analysis of this data. I tested mi, Amelia, and MissForest as alternative packages in order to impute the missing data. I now have 5 separate datasets with the variables I am interested in factor analysing. In my reading of the package

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