similar to: Panel data and imputed datasets

Hi all, I want to check if this is a bug for which I should file a report. I am using R2.5.0 on OS X 10.4.9. When I invoke the data editor and when I change the values of individual cells, it seems to work as intended. However, when I try to delete/add a row/column, R.app crashes. I've attached the crash log. Best, -Nathan -------------- next part -------------- An embedded and

Hi all, I am trying to use the functions in the plm package with multiply imputed datasets. I had tried to combine the datasets using the imputationList() function of mitools. plm, however, requires a data argument, and I don't know where to point it to. I'd appreciate any help people might have. A (possible) fuller description of the problem and code is in a previous

Hi all, I am trying to run the mice package (for multiple imputation) on a data frame that is 5174 x 100. When I run mice(frame), I get the following response: Error in solve.default(t(xobs) %*% xobs) : Lapack routine dgesv: system is exactly singular and execution stops. I'm no expert at matrix algebra, so if someone could explain this to me and what I can do to get around it,

Hi everyone, thanks for taking the time to read my post. I am using ubuntu 8.04 DRBL in an IT setting that has wine 1.1.16 installed with Spybot S&D. We use it to PXE boot virus-infected computers to scan them without threatening a real installation, and without the hassle of live-cds. Spybot has a switch (/allhives) that detects multiple windows installations for scanning, and the switch

Dear All, I am doing a growth modeling using NLME. I have three levels in my data: observation, individual, household. About half of my total sample have missing values in my household-level covariates. Under this situation, the best way to go is probably to multiply impute the data (for, say, 5 times), estimate the same model separately on each model using LME function, and merge the results. My

Hi, I'm using the library MICE to make multiple imputations. I'can pool the results to show how the predicted values fit, but how to combine the five imputed datasets? I take the mean? for exemple : x1<-complete(imp) x2<-complete(imp, 2) x3<-complete(imp, 3) x4<-complete(imp, 4) x5<-complete(imp, 5) impcomp<-(x1+x2+x3+x4+x5)/5 Or there is an other way to do this?

Hi, Does anybody know how to obtain the imputed SNP genotype probabilities from the snpStats package? I am interested in using an imputation method implemented in R to be further used in a simulation study context. I have found the snpStats package that seems to contain suitable functions to do so. As far as I could find out from the package vignette examples and its help, it gives the

Hello, All, GWAF 1.2 R.Version() is below. system(lme.batch.imputed( phenfile = 'phenfile.csv', genfile = 'CARe_imputed_release.0.fhsR.gz', pedfile='pedfile.csv', phen='phen1', covar=c('covar1','covar2'), kinmat='imputed_fhs.kinship.RData', outfile='imputed.FHS.IBC.GWAF.LME.output.0.txt' )) Gives the error messages: Error in

Dear R users I want to combine multiply imputed data-sets generated from mice to do classfication. However, I have various questions regarding the use of mice library. For example suppose I want to predict the class in this data.frame: data(nhanes) mydf=nhanes mydf$class="pos" mydf$class[sample(1:nrow(mydf), size=0.5*nrow(mydf))]="neg" mydf$class=factor(mydf$class) First I

Dear All, I've used mice package for my latent class analysis and binary logistic regression I've imputed five data sets and with long format I've added new variable that shows latent class membership. And then in addition to other variables, I'll use binary logistic regression and try to pool the estimates. However I couldn't create data.frame to mids objects, and therefore

Hi folks, I have five imputed data sets and would like to apply the same recoding routines to each. I could do this sort of thing pretty easily in Stata using MIM, but I've decided to go cold turkey on other stats packages as a incentive for learning more about R. Most of the recoding is for nominal variables, like race, religion, urbanicity, and the like. So, for example, to recode race

I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666

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