similar to: missing values imputation

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

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

Dear R-helpers, I have a problem which I suppose is trivila, but... I have included NA values as factors ( (to be able to make nice printed summaries with NAs % ba category ) with the following code dat$x.f<-factor(dat$x, exclude=NULL); levels(dat$x.f)<-c("A1","A2","A3","A4","NA"); length(dat$x.f) Now, I want to impute the missing values.

hello to all! I have a small problem wit predict() for lm Let's say I have predictors x1 and x2, response y I want to predict for a new ds say dn<-data.frame(x1= seq(min(x1),max(x1),length=10),x2=rep(median(x2),10)) predict(lm(y~x1+x2),dn,se.fit=T) Error message > Error: variables 'x1', 'x2' were specified differently from the fit (I looked in the help and found

********************************************************************** This email and any files transmitted with it are confidential and intended solely for the use of the individual or entity to whom they are addressed. If you have received this email in error please notify the system manager. ********************************************************************** Hi Just installing R and some

Hello R helpers! I looked but did not find a table-lookup R-utility. I could use a loop to do the job (old FORTRAN/C habits die hard) but if I have a big table in which I have to search for the values corresponding to a vector, I end up logically with a double loop. Is there already such a utility? Otherwise, is there a way without loops? Thanks as always Anne

Sorry, sorry.... of course levels(testf)[c(2,1,3)] will do the job My excuses to all Anne PS I will meditate the following saying "la parole est d'argent et le silence est d'or" BONNES FETES A TOUS SEASONAL GREETINGS ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com

Is there an upgrate of the vcd library (visualisation of categorical data) for the latest R version? Trying to download it from CRAN I get URL /data/WWW/ftp/pub/R/bin/windows/contrib/r-release/vcd_0.1-3.4.zip was not found on this server. googling it, I found it for instance on http://www.sourcekeg.co.uk/cran/bin/windows/contrib/1.9/ but trying to install it gave me the message >

Hello! I would like to know if there is a simple way to reorder levels of a given factor.Let's say that the vector testf<-factor(c("red","red","red","blue","blue","white")) levels(testf) : blue red white should have reordered levels such as levels(testf) : red blue white (this is for presentation purposes) I guess

Hi! Is there an easy way to add to the scree-plot labels to each value pertaining to the cumulative proportion of explained variance? Thanks and a happy new year Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com --------------------------------------------------- M-TD Modelling and Technology Development PSE-C

Hi! I'm trying to do some ploytomous logistic regression using multinom() in the nnet package, but am a bit confused about interpretation of the results Is it possible to get the following quantities: I: maximum likelihood estimates to test for fit of model and significance of each predictor (I would like to produce a table of the following type) Analysis of Variance: MLE (values are

Hello R helpers! I'm using the avas function form package acepack (called from areg.boot package Hmisc) to estimate automatically transformations of predictors (in this case monotonous) and response. Well, it seems to work quite well, but I have 3 basic questions: - which set of basis functions is used in this procedure? - how do I back transform my estimate (y hat ) to the originasl scale?

Hi all, I still have problems with the predict function by setting up the values on which I want to predict ie: original df: p1 (193 obs) variates y x1 x2 rm(list=ls()) x1<-rnorm(193) x2<-runif(193,-5,5) y<-rnorm(193)+x1+x2 p1<-as.data.frame(cbind(y,x1,x2)) p1 y x1 x2 1 -0.6056448 -0.1113607 -0.5859728 2 -4.2841793 -1.0432688 -3.3116807 ...... 192

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.

Hi R helpers! I'm still a bit ( alot) confused by the use of "inverseTrans" and "ytype" in areg.boot (Hmisc): What I want to do seems very simple, but I do not get the result I want: plot the predicted values in the original scale. (I did not understand the documentation, sorry!) for instance the following code

Another newbie question.... I want to create a factor (say cT)from a numerical variable (sy temp) by regrouping the values in classes (say cT <390, [390,400[,[400,409[...>=550) Is there a simple way of doing that using the factor function? AND I do not find how to manipulate strings (I want to concatenete characters strings ("abkdas","chjw") into something like

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 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

Dear R-users, I want to multiple impute missing scores, but only for a few subgroups in my data (variable 'subgroups': only impute for subgroups 2 and 3). Does anyone knows how to do this in MICE? This is my script for the multiple imputation: imp <- mice(data, m=20, predictorMatrix=pred, post=post, method=c("", "", "", "",

I'm about to present a report (for internal use of governmental agency). I used extensively R , contibuted packages, as well as communications on the R-list As well as citing R, I would like to know how to cite the contributed packages (it is not so easy, as some have been used exensively, other marginally, some are called from another package and some were not used as softwares but gave me