similar to: EM for missing data

Displaying 20 results from an estimated 10000 matches similar to: "EM for missing data"

2012 Jul 21
2
combined EM dataset for missing data?
Hi list, I am wondering if there is a way to use EM algorithm to handle missing data and get a completed data set in R? I usually do it in SPSS because EM in SPSS kind of "fill in" the estimated value for the missing data, and then the completed dataset can be saved and used for further analysis. But I have not found a way to get the a completed data set like this in R or SAS. With
2010 Jun 30
3
Logistic regression with multiple imputation
Hi, I am a long time SPSS user but new to R, so please bear with me if my questions seem to be too basic for you guys. I am trying to figure out how to analyze survey data using logistic regression with multiple imputation. I have a survey data of about 200,000 cases and I am trying to predict the odds ratio of a dependent variable using 6 categorical independent variables (dummy-coded).
2009 Apr 24
1
Multiple Imputation in mice/norm
I'm trying to use either mice or norm to perform multiple imputation to fill in some missing values in my data. The data has some missing values because of a chemical detection limit (so they are left censored). I'd like to use MI because I have several variables that are highly correlated. In SAS's proc MI, there is an option with which you can limit the imputed values that are
2008 Jun 30
3
Is there a good package for multiple imputation of missing values in R?
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
2012 Mar 30
3
pooling in MICE
Hi everyone, Does anyone here has experience using MICE to impute missing value? I am having problem to pool the imputed dataset for a MANOVA test, could you give me some advice please? Here is my code: > library(mice) >
2010 Dec 22
3
Help with Amelia
Hi I have used the amelia command from the Amelia R package. this gives me a number of imputed datasets. This may be a silly question, but i am not a statistician, but I am not sure how to combine these results to obtain the imputed dataset to usse for further statistical analysis. I have looked through the amelia and zelig manuals but still can not find the answer. This maybe because I dont
2011 Jan 31
2
Rubin's rules of multiple imputation
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.
2012 Jun 03
1
Multiple imputation, multinomial response & random effects
Dear R-group, Could somebody recommend a package that can deal with a multinomial response variable (choice of breeding tactic in mice, which has four unordered levels), multiply-imputed data (generated using the Amelia package) and two non-nested random effects: individual identity (133 individuals made up to four choices each) and year (for which there are six levels and sample size varies
2012 Jan 13
2
Averaging over data sets
Hi all, after using Amelia II to create 10 imputed data sets I need to average them to have one unique data that includes the average for each cell of the variables imputed, in addition to the values for the variables not imputed. Such data has many variables (some numeric, other factors), and more than 20000 observations. I do not know how to average them out. Any help? Below I provide a small
2007 Jun 07
1
MITOOLS: Error in eval(expr, envir, enclos) : invalid 'envir' argument
R-users & helpers: I am using Amelia, mitools and cmprsk to fit cumulative incidence curves to multiply imputed datasets. The error message that I get "Error in eval(expr, envir, enclos) : invalid 'envir' argument" occurs when I try to fit models to the 50 imputed datasets using the "with.imputationList" function of mitools. The problem seems to occur
2012 Jun 18
1
Package of EM and MI for IRT in R
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.
2012 Dec 08
1
imputation in mice
Hello! If I understand this listserve correctly, I can email this address to get help when I am struggling with code. If this is inaccurate, please let me know, and I will unsubscribe. I have been struggling with the same error message for a while, and I can't seem to get past it. Here is the issue: I am using a data set that uses -1:-9 to indicate various kinds of missing data. I changed
2012 Oct 30
1
Amelia imputation - column grouping
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
2008 Nov 04
2
ordered logistic regression of survey data with missing variables
Hello: I am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey
2008 Nov 26
1
multiple imputation with fit.mult.impute in Hmisc - how to replace NA with imputed value?
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
2011 Aug 01
1
Impact of multiple imputation on correlations
Dear all, I have been attempting to use multiple imputation (MI) to handle missing data in my study. I use the mice package in R for this. The deeper I get into this process, the more I realize I first need to understand some basic concepts which I hope you can help me with. For example, let us consider two arbitrary variables in my study that have the following missingness pattern: Variable 1
2008 Feb 11
1
Help with write.csv
Dear all, I am new to R. I am using the impute package with data contained in csv file. I have followed the example in the impute package as follows: > mydata = read.csv("sample_impute.csv", header = TRUE) > mydata.expr <- mydata[-1,-(1:2)] > mydata.imputed <- impute.knn(as.matrix(mydata.expr)) The impute is succesful. Then I try to write the imputation results
2011 Oct 18
1
getting basic descriptive stats off multiple imputation data
Hi, all, I'm running multiple imputation to handle missing data and I'm running into a problem. I can generate the MI data sets in both amelia and the mi package (they look fine), but I can't figure out how to get pooled results. The examples from the mi package, zelig, etc., all seem to go right to something like a regression, though all I want are the mean and SE for all the
2012 Aug 20
1
Combining imputed datasets for analysis using Factor Analysis
Dear R users and developers, I have a dataset containing 34 variables measured in a survey, which has some missing items. I would like to conduct a factor analysis of this data. I tested mi, Amelia, and MissForest as alternative packages in order to impute the missing data. I now have 5 separate datasets with the variables I am interested in factor analysing. In my reading of the package
2011 Jul 20
1
Calculating mean from wit mice (multiple imputation)
Hi all, How can I calculate the mean from several imputed data sets with the package mice? I know you can estimate regression parameters with, for example, lm and subsequently pool those parameters to get a point estimate using functions included in mice. But if I want to calculate the mean value of a variable over my multiple imputed data sets with fit <- with(data=imp, expr=mean(y)) and