similar to: imputation

Dear all, I am pleased to announce the release of a new package named 'clusterMI' on CRAN. clusterMI allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps: 1. missing data imputation using tailored imputation models: four multiple imputation methods are proposed, two are

Dear all, I am pleased to announce the release of a new package named 'clusterMI' on CRAN. clusterMI allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps: 1. missing data imputation using tailored imputation models: four multiple imputation methods are proposed, two are

Dear all, I am trying to impute data for a range of variables in my data set, of which unfortunately most variables have missing values, and some have quite a few. So I set up the predictor matrix to exclude certain variables (setting the relevant elements to zero) and then I run the imputation. This works fine if I use predictive mean matching for the continous variables in the data set. When I

Hello, this is not really an R-related question, but since the posting guide does not forbid asking non-R questions (even encourages it to some degree), I though I'd give it a try. I am currently doing some secondary analyses of the PISA (http://pisa.oecd.org) student data. I would like to treat missing values properly, that is using multiple imputation (with the mix package). But I am not

Hi all, I have a question about multiple imputation within the MICE package. I want to use passive imputation for my variable called X, because it is calculated out of multiple variables, namely Y, Z. Let's give an example with BMI. I know, that if I want to use passive imputation for BMI, I can use the following command: meth["BMI"] <- "~I(weight/(height/100)^2)"

Dear list, this is perhaps more of a statistics question than an R question, but perhaps someone could help me out anyway. I'm doing sociological research and am currently in the process of familiarizing myself with the basic concepts of multiple imputation. Eventually, my goal is to perform quantile regression on a large data set, where one non-negative discrete variable contains missing

Colleagues, I have started working with Amelia, with the aim of imputing missing data for time-series data. Although I have succeeded in getting Amelia to perform the imputation, I have not found any documentation describing how Amelia imputes time-series data. I have read the basic Amelia documentation, but it does not address how time-series data are imputed. The documentation describes

Hi, I am working with a multiple-item questionnaire. I have previously done item-level multiple imputation using MICE in R and right now I am attempting duplicate-scale imputation based on the guidelines listed in Enders's applied missing data analysis book. I use MICE to do MI as it allows me to specify school effect as I am working with multilevel data; my respondents come from different

I am running into a problem using 'mix' for multiple imputation (over continuous and categorical variables). For the way I will be using this I would like to create an imputation model on some training data set and then use this model to impute missing values for a different set of individuals (i.e. I need to have a model in place before I receive their information). I expected that all

Hi, I wrote a script in order to simulate data, which I will use for evaluating missing data and imputation. However, I'm having trouble with the last part of my script, in which a dataframe is constructed without missing values. This is my script: y1 <- rnorm(10,0,3) y2 <- rnorm(10,3,3) y3 <- rnorm(10,3,3) y4 <- rnorm(10,6,3) y <- c(y1,y2,y3,y4) a1 <-rep(1,20) a2

Dear R-List, I apologize for not posting a reproducible example - the reason is that I actually do not succeed in reproducing my specific problem with generated data. Let me still describe the problem: I want to impute missing data using the "pan" package. a) It works, when I use a fraction of my data (e.g. 200 out of 44000 cases) b) It works, when I generate a dataset of equal

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 always avoided missing data by keeping my distance from the real world. But I have a student who is doing a study of real patients. We're trying to test regression models using multiple imputation. We did the following (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of

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 <-

Hi everybody, I am quite new to data imputation, but I would like to use the R package ' Amelia II: A Program for Missing Data '. However, its unclear to me how the input for amelia should look like: I have a data frame consisting of numerous coulmns, which represent different experimental conditions, whereby each column has 3 replicates. I want amelia to perform an imputation across

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

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

Hi everyone, I am looking for a package in R which can help me in using the imputation technique to find the missing values for my regression analysis. Any help would be appreciated. Cheers Arun -- View this message in context: http://www.nabble.com/Imputation-Packages-tp15790749p15790749.html Sent from the R help mailing list archive at Nabble.com.

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

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("", "", "", "",