similar to: Shafer's MI software for S-plus

Dear all! I'd like to do multiple imputation of missing values with s-plus libraries that are provided by Shafer (http://www.stat.psu.edu/~jls/misoftwa.html). I wonder, whether these libraries are compatible or somehow convertible to R (because I don't have S-plus), so that I can use this functions using the R Program. I would be happy if you could tell me, -if it is possible to use

Hi, I have had a quick search to see whether this question has been asked/answered before but haven't found anything directly related to it. Basically, I am wondering if I can run the packages, developed by Shafer for S-Plus, that allow multiple imputation of missing data - NORM, CAT, MIX, and PAN. If not, does anyone know if someone has done the modification that would make these packages

Hi Folks, I'm using the 'norm' package (based on Shafer's NORM) on some data. In outline, (X,Y) are bivariate normal, var(X)=0.29, var(Y)=24.4, cov(X,Y)=-0.277, there are some 900 cases, and some 170 values of Y have been set "missing" (NA). The puzzle is that, repeating the multiple imputation starting from the same random seed, I get different answers from the repeats

Hi Folks, I'm currently looking at missing data/imputation methods (including multiple imputation). S-Plus has a "missing data library". What similar resources are available within R? Or does one roll one's own? Best wishes to all, Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk>

I'm discovering R (very impressive), and didn't find in the docs a simple method for replacing, in a data frame, missing values (NA) with the column's mean (or any other method for reconstructing missing values when needed). Thanks in advance for your help. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi Folks, Fernando Tussell and I have been working on an R package of Shafer's CAT software for S-plus, for multiple imputation of categorical data. A very first version of this ("0.0-1") now seems to work, in that blatant bugs and segfaults seem to have been worked around. It now needs some testing in the wild, so if anyone would like to have a copy of cat_0.0-1.tar.gz to try out

Joe Schafer has some imputation libraries in S plus, and one of them is even on stand alone for Windows. Has anybody attempted to write these very useful packages in R? Jose Ramon Albert Manila, Philippines -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

I am trying to use the mi package to impute data, but am running into problems with the functions it calls. For instance, I am trying to impute a categorical variable called "min.func." The mi() function calls the mi.categorical() function to deal with this variable, which in turn calls the nnet.default() function, and passes it a fixed parameter MaxNWts=1500. However, as

Hello everyone, The topic of whether or not LLVM allows for infinite loops has come up a lot recently (several times this week already). Regarding motivation, there are two important facts: 1. Some languages, such as Java, have well-defined infinite loops. See: http://docs.oracle.com/javase/specs/jls/se7/html/jls-17.html#jls-17.4.9 and:

----- Original Message ----- > From: "Chandler Carruth" <chandlerc at google.com> > To: "Hal Finkel" <hfinkel at anl.gov>, "LLVM Dev" <llvmdev at cs.uiuc.edu> > Sent: Thursday, July 16, 2015 1:00:05 AM > Subject: Re: [LLVMdev] [RFC] Defining Infinite Loops > > > FWIW, I'm very much in favor of having a firm and clear answer

Here is a problem that perhaps someone out here has an idea about. It vaguely reminds me of something I've seen before, but can't place. Can anyone help? For multiple imputation, there are simpler methods available if the patterns of missing data are 'monotone' --- if Vj is missing then all variables Vk, k>j are also missing, vs. more complex methods required when the

Hello all. I am trying to run PAN, multilevel multiple imputation program, in R to impute missing data in a longitudinal dataset. I could successfully run the multiple imputation when I only imputed one variable. However, when I tried to impute a time-varying covariate as well as a response variable, I received an error message, “Error: subscript out of bounds.” Can anyone tell if my commands

Dear all members, I am Phd. candidate student at Chulalongkorn U., Thailand. I am interested in expectation maximization algorithm (EM) and multiple imputation (MI) for imputation missing values(missing at random(MAR) and missing not at random (MNAR)) in IRT (3-PL). So, I want to know about package in R or function of EM and MI for simulate this problem I am looking forward your answer.

I'm trying to impute data using the mi package, but after running through almost the entire first round of imputations (which takes quite a while), it throws this error (I'll include the whole output prior to the error for context). Does anyone know what is causing it, or how I can fix it? More specifically, how can I tell what is throwing the error so I know what to fix? Is

Hi, I could not find an R package for the analysis of multivariate normal datasets with missing values. Prof. Joseph Schafer has created an S+ library (norm) that does such type of analysis, which I now ported to R. I guess that I should have asked here if there are other people working on such project, before I actually ported it, but... If such package has not been ported, I will upload the

Hi all, I'm currently working with a dataset that has quite a few missing values and after some investigation I figured that multiple imputation is probably the best solution to handle the missing data in my case. I found several references to functions in S-Plus that perform multiple imputation (NORM, CAT, MIX, PAN). Does R have corresponding functions? I searched the archives but was not

Dear alltogether, I experience very strange behavior of imputation of NA's with the NORM library. I use R 2.2.0, win32. The code is below and the same dataset was also tried with MICE and aregImpute() from HMISC _without_ any problem. The problem is as follows: (1) using the whole dataset results in very strange imputations - values far beyond the maximum of the respective column, >

Hi In a dataset I have length and age for cod. The age, however, is ony given for 40-100% of the fish. What I need to do is to fill inn the NAs in a correct way, so that age has a value for each length. This is to be done for each sample seperately (there are 324 samples), meaning the NAs for sampleno 1 shall be calculated from the known values from sampleno 1. As for example length 55 cm

Hi, I have a big dataset which has many missing values and want to implement Multiple imputation via Monte carlo markov chain by following J Schafer's "Analysis of incomplete multivariate data". I don't know where to begin and is looking for a sample R code that implements multiple imputation with EM, MCMC, etc.... Any help / suggestion will be greatly appreciated. David

Hello R experts I am using Hmisc aregImpute and Impute (following example on page 105 of The Hmisc and Design Libraries). *My end goal is to have NAs physically replaced in my dataframe. I have read the help pages and example in above sited pdf file, but to no avail. Here is example of what I did. Ph, my data frame, is attached. > xt <- aregImpute (~ q5 + q22rev02 + q28a, n.impute=10,