similar to: combining n imputed dataset

Hi all, I am hardly an expert, so I expect that this code is not the easiest/ most efficient way of getting where I want. Any suggestions in that direction would also be helpful. I am working on panel analysis with five imputed datasets, generated by Amelia. To do panel analysis, it seemed that the plm package was the best, providing a convenient wrapper for fixed and random effects

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

Dear R-Users, The following question is more of general nature than a merely technical one. Nevertheless I hope someone get me some answers. I have been using the mice package to perform the multiple imputations. So far, everything works fine with the standard regressions analysis. However, I am wondering, if it is theoretically correct to perform nonparametric models (GAM, spline

Hello, I have a general question about combining imputations as well as a question specific to the rms and Hmisc packages. The situation is multiple regression on a data set where multiple imputation has been used to give M imputed data sets. I know how to get the combined estimate of the covariance matrix of the estimated coefficients (average the M covariance matrices from the individual

Hello! If I understand this listserve correctly, I can email this address to get help when I am struggling with code. If this is inaccurate, please let me know, and I will unsubscribe. I have been struggling with the same error message for a while, and I can't seem to get past it. Here is the issue: I am using a data set that uses -1:-9 to indicate various kinds of missing data. I changed

Dear All, I am currently using the MI package (Su, Gelman, Hill and Yajima) to make multiple Imputations of my dataset with missing values. After fitting a model, I can use display(model) to visualize the pooled estimates as well as estimates of each imputed dataset. I can visualize these also by typing print(model). However I would like to know how I can extract estimates of single imputed

From: <david_foreman at doctors.org.uk> Subject: [R] Re: extracting datasets from aregImpute objects To: <r-help at stat.math.ethz.ch> Message-ID: <1092391719_117440 at drn10msi01> Content-Type: text/plain; charset="us-ascii" I've tried doing this by specifying x=TRUE, which provides me with a single imputation, that has been useful. However, the help file

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 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

Dear all, I have been attempting to use multiple imputation (MI) to handle missing data in my study. I use the mice package in R for this. The deeper I get into this process, the more I realize I first need to understand some basic concepts which I hope you can help me with. For example, let us consider two arbitrary variables in my study that have the following missingness pattern: Variable 1

Hello all, I am trying to impute some missing data using the mice package. The data set I am working with contains 125 variables (190 observations), involving both categorical and continuous data. Some of these variables are missing up to 30% of their data. I am running into a peculiar problem which is illustrated by the following example showing both the original data (blue) and the imputed

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

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

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 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 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

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

Hi, I want to impute the missing values in my data set multiple times, and then combine the results (like multiple imputation, but manually) to get a mean of the parameter(s) from the multiple imputations. Does anyone know how to do this? I have the following script: y1 <- rnorm(20,0,3) y2 <- rnorm(20,3,3) y3 <- rnorm(20,3,3) y4 <- rnorm(20,6,3) y <- c(y1,y2,y3,y4) x1 <-

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

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