similar to: Converting S-Plus Libraries to R

Greetings folks, Shafer's S-plus package "norm" for multiple imputation of missing values in multivariate normal data has been most kindly and usefully ported to R by Alvaro A. Novo. Shafer's website http://www.stat.psu.edu/~jls/ lists four S-plus packages in all: NORM - multiple imputation of multivariate continuous data CAT - multiple imputation of multivariate

Dear R expert I am a student and I am currently conducting a research project on the Modeling Loss Index Triggers to price Cat Bonds: Application of the risk of hurricanes in USA. I need to solve with R (especially with EM algorithm) this specific problem below. CRAN Package archive doesn't seem to have it also the statistical modeling journal didn't contain a paper that implements this:

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, 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, 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

What R functionnalities are there to do missing values imputation (substantial proportion of missing data)? I would prefer to use maximum likelihood methods ; is the EM algorithm implemented? in which package? Thanks Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com

I am looking for a book that discusses the theory of multiple imputation (and other methods of dealing with missing data) and, just as importantly, how to implement these methods in R or S-Plus. Ideally, the book would have a structure similar to Faraway (Regression), Pinheiro&Bates (Mixed Effects) and Wood (GAMs) and would be very modern (i.e. published within the last couple of years).

Dear R-help, I am trying to impute missing data for the first time using R. The norm package seems to work for me, but the missing values that it returns seem odd at times -- for example it returns negative values for a variable that should only be positive. Does this matter in data analysis, and/or is there a way to limit the imputed values to be within the minimum and maximum of the actual

Hi! I am a relatively new user of R and I use it to prepare my dissertation. I have come to some very usefull and specific libraries written for S-PLUS 4 and would like to use them in R. Is that possible? I just found out that one of these libraries has already been transfered to R, while 3 others have not. For the matter of beeing more exact I''m interested in the dealing with missing

I'm looking for a port of Schafer's PAN module for multiple imputation of nested data. It is written in S-Plus, and I would like to use it in R. Any pointers most appreciated. Best wishes, Paul von Hippel

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,

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>

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:

Hello, I'm looking for a more descriptive manual/tutorial/paper for the pan package. The provided manual and example do not contain any useful hints how to specify a model with more than one variable and leaves several questions unanswered. This also applies to the referred paper "Schafer: Imputation of missing covariates under a multivariate linear mixed model." Can anyone

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, Evgenia and I have copied an updated version of the mlbench package to CRAN which contains several new data sets. We have also changed some of the variable names to avoid name conflicts. Best, -- ------------------------------------------------------------------- Friedrich Leisch Institut f?r Statistik Tel: (+43 1) 58801 10715 Technische

Hi, Evgenia and I have copied an updated version of the mlbench package to CRAN which contains several new data sets. We have also changed some of the variable names to avoid name conflicts. Best, -- ------------------------------------------------------------------- Friedrich Leisch Institut f?r Statistik Tel: (+43 1) 58801 10715 Technische

----- 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

> You dropped some context... > A daemon program wouldn't be readonly. An infinite loop can be. Right. To prevent miscommunication, here is a quick analysis of a problematic (IMO) example: We start with ``` define void @infloop(i1 %c) { entry: br i1 %c, label %l, label %e l: br label %l e: ret void } define void @main_func() { entry: call void @infloop(i1 1) ret

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.