similar to: Error when running duplicate scale imputation for multilevel data

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

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

I have been using lme to model data with multiple nested random effects and continuous response variables however I also have data with a binary response variable, binomial errors and multiple levels of nesting of random effects (e.g. site/block/quadrat/year), is there a package available which will do this? Jim Lindsey's package "repeated" appears to be only able to cope with 2

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 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

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)"

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

Hello all. I am trying to run PAN, multilevel multiple imputation program, in R to impute missing data in a longitudinal dataset. I could successfully run the multiple imputation when I only imputed one variable. However, when I tried to impute a time-varying covariate as well as a response variable, I received an error message, “Error: subscript out of bounds.” Can anyone tell if my commands

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 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

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",

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

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

Hi, I'm relatively new to R and it'll be great if someone can help me with what I'm doing here. I am trying to do multiple imputation on my dataset, but I'm not quite sure which function to use as my dataset contains dichotomous variables. Here's an outline of what i've done so far, and i'm not sure if i'm doing it right, and where to go from here. It'll be

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

Dear R-users, Version V2.0 of the package mice is now available on CRAN for Windows, Linux and Apple users. Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Specifcation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection,

Dear R-users, Version V2.0 of the package mice is now available on CRAN for Windows, Linux and Apple users. Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Specifcation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection,

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

Dear list members, how can multiple imputation realized for anova tables in R? Concretely, how to combine F-values and R^2, R^2_adjusted from multiple imputations in R? Of course, the point estimates can be averaged, but how to get standarderrors for F-values/R^2 etc. in R? For linear models, lm.mids() works well, but according to Rubins rules, standard errors have to be used together with