similar to: Imputing NAs using transcan(); impute()

Dear all, I am using transcan to impute missing values (single imputation). I have several dichotomous variables in my dataset, but when I try to impute the missings sometimes values are imputed that were originally not in the dataset. So, a variable with 2 values (severe weight loss or no/limited weight loss) for example coded 0 and 1, shows 3 different values after imputation (0, 1 and 2). I

Hello: I have been trying to impute missing values of a data frame which has both numerical and categorical values using the function transcan() with little luck. Would you be able to give me a simple example where a data frame is fed to transcan and it spits out a new data frame with the NA values filled up? Or is there any other function that i could use? Thank you avneet ===== I believe in

I would like to impute missing data in a set of correlated variables (columns of a matrix). It looks like transcan() from Hmisc is roughly what I want. It says, "transcan automatically transforms continuous and categorical variables to have maximum correlation with the best linear combination of the other variables." And, "By default, transcan imputes NAs with "best

Hello, I'm working on a Cox Proportional Hazards model for a cancer data set that has missing values for the categorical variable "Grade" in less than 10% of the observations. I'm not a statistician, but based on my readings of Frank Harrell's book it seems to be a candidate for using multiple imputation technique(s). I understand the concepts behind imputation, but using

  I have timeseries data for some factors, and some missing values are there in those factors, I want impute those missing values without disturbing the distribution of that factor, and maintaining the correlation with other factors. Pl. suggest me some imputation methods. I tried some functions in R like aregImpute, transcan. After the imputation I am unable to retrive the data with imputed

Folks, This must be a rather common problem with real life time series data but I don't see anything in the archive about how to deal with it. I have a time series of natural gas prices by flow date. Since gas is not traded on weekends and holidays, I have a lot of missing values, FDate Price 11/1/2006 6.28 11/2/2006 6.58 11/3/2006 6.586 11/4/2006 6.716 11/5/2006 NA 11/6/2006 NA 11/7/2006

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

Dear all; I am trying to use HMISC imputation function to perform multiple imputations on my data and I keep on getting errors for the code given in the help files. When using "aregImpute" the error is; >f <- aregImpute(~y + x1 + x2 + x3, n.impute=100) Loading required package: acepack Iteration:1 Error in .Fortran("wclosepw", as.double(w), as.double(x),

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

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'

I'm looking for a package that has a start-of-the-art method of imputation of missing values in a data frame with both continuous and factor columns. I've found transcan() in 'Hmisc', which appears to be possibly suited to my needs, but I haven't been able to figure out how to get a new data frame with the imputed values replaced (I don't have Herrell's book). Any

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,

ear all, This is my first time using this listserv and I am seeking help from the expert. OK, here is my question, I am trying to use impute.knn function in impute library and when I tested the sample code, I got the error as followingt: Here is the sample code: library(impute) data(khanmiss) khan.expr <- khanmiss[-1, -(1:2)] ## ## First example ## if(exists(".Random.seed"))

Hello, I would like to use the aregImpute and fit.mult.impute to impute missing values for my dataset and then conduct logistic regression analyses on the data, taking into account that we imputed values. I have no problems imputing the values using aregImpute, but I am getting an error at the fit.mult.impute stage. Here is some sample code (I actually have more observations and variables to

[CC:ing package maintainer of 'impute' package and crossposting to r-devel and bioc-devel because this affects both audiences] Hi, the 'impute' package is published both on CRAN and Bioconductor; http://cran.r-project.org/web/packages/impute/ http://bioconductor.org/packages/2.3/bioc/html/impute.html The one on CRAN is v1.0-5, and the one on BioC is v1.14.0. The two

Hello! I am trying to impute missing data and output the results of the imputation. My data set is called: MyData. I have a bunch of variables all of which start with Q20_ - and some of them have missing values. Here is what I've been doing: imputationmodel<-mice( MyData[ c (grep("Q20_", names(MyData)) ) ] ) multipledataset<-complete(imputationmodel,action="long")

Dear list, I have a question about imputing 2 level data in MICE, could you give me some suggestions please? Thank you very much. The data set contains 35634 cases and 1007 variables, 280 of them are categorical variables, and the rest of them are continuous variables. On the second level, there are 198 units. I am trying to impute missing values for 270 categorical variables by using the

Hi all (and especially Frank), I'm trying to use x=T, y=T in order to run a validated stepwise cox regression in rsm, having multiply imputed using mice. I'm coding model.max<-fit.mult.impute(baseform,cph,miced2,dated.sexrisk2,x=T,y=T) baseform is baseform<-Surv(si.age,si=="Yes")~ peer.press + copy.press + excited + worried + intimate.friend + am.pill.times +

Dear all, I used fit.mult.impute in Dr. Harrell's Design package to fit a cox ph regression model on five imputed datasets, where all missing predictors were filled by multiple imputation using R package Mice. Are there any functions able to do bootstrapping or cross-validation for the aggregated model? I tried function 'validate' and 'calibrate' in Design package, but

Hello R-help, Thanks for everyone's very helpful suggestions so far. I am now trying to use aregImpute for my missing data imputation. Here are the code and error messages. Any suggestions would be very much appreciated. Sincerely, Anders Corr ######################################## #Question for R-Help on aregImpute ######################################## #DOWNLOAD DATA (61Kb)