similar to: multiple imputation

R 2.7.2 PPC Mac OS X 10.4.11 library mice 1.13.1 I try to use mice for multivariate data imputation. My variables are numeric, factors, count data, ordered factors. First I created a vector for the methods to use with each variable ImpMethMice<-c(rep("logreg", 62), rep("polyreg",1), rep("norm",12), rep("polyreg",12)) next step was

Hi I am unable to impute using the MICE command in R when imputing a binary variable using linear discriminant analysis. To illustrate my problem I have created a dataset, which consists of 1 continuous and 1 binary variable. The continuous variable is complete and the binary variable is partially observed. I am able to impute using the MICE command where the imputation methods is logistic

Dear list, I'd like to use multiple imputations to try and save a somewhat badly mangled dataset (lousy data collection, worse than lousy monitoring, you know that drill... especially when I am consulted for the first time about one year *after* data collection). My dataset has 231 observations of 53 variables, of which only a very few has no missing data. Most variables have 5-10% of

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

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

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 all, I am trying to impute some missing data using the mice package. The data set I am working with contains 125 variables (190 observations), involving both categorical and continuous data. Some of these variables are missing up to 30% of their data. I am running into a peculiar problem which is illustrated by the following example showing both the original data (blue) and the imputed

Any suggestions on the following would be grateful. I'm trying to impute data, where a fictitional dataset is defined as... set.seed(110) n <- 500 test <- data.frame(smoke_status = rbinom(n, 2, 0.6), smoke_amount = rbinom(n, 2, 0.5), rf1 = rnorm(n), rf2 = rnorm(n), outcome = rbinom(n, 1, 0.3)) # smoke_status (0, 1, 2) is c("non-smoker, "ex-smoker",

Hi I am trying to impute missing values for my data.frame. As I intend to use the complete data for prediction I am currently measuring the success of an imputation method by its resulting classification error in my training data. I have tried several approaches to replace missing values: - mean/median substitution - substitution by a value selected from the observed values of a variable - MLE

Dear list, Is there any one use MICE package deal with multilevel missing values here? I have a question about the 2lonly.pmm() and 2lonly.norm(), I get the following error quite often. Here is the code the error, could you give me some advice please? Am I using it in the right way? > ini=mice(bhrm,maxit=0) > pred=ini$pred > pred V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15

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

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

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

I have fitted a proportional odds model, but would like to compare it to a continuation ratio model. However, I am unable to fit the CR model _including_ imputated data. I guess my troubles start with settuping the data for the CR model. Any hint is appreciated! Christian library(Hmisc) library(rms) library(mice) ## simulating data (taken from rms::residuals.lrm) set.seed(1) n <- 400 age

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

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)"

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous

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

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

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