similar to: A reproducibility puzzle with NORM

Dear alltogether, I experience very strange behavior of imputation of NA's with the NORM library. I use R 2.2.0, win32. The code is below and the same dataset was also tried with MICE and aregImpute() from HMISC _without_ any problem. The problem is as follows: (1) using the whole dataset results in very strange imputations - values far beyond the maximum of the respective column, >

Greetings! I am bootstrapping and I am using EM in the norm package to fill in missing data for a financial time series with each step of the loop. For the most part EM works fine for me, but the following error message is guaranteed before I hit the 200th scenario: Iterations of EM: 1...2...3........348...349...Error: NA/NaN/Inf in foreign function call (arg 2) The following code should

Dear R-help, I am trying to impute missing data for the first time using R. The norm package seems to work for me, but the missing values that it returns seem odd at times -- for example it returns negative values for a variable that should only be positive. Does this matter in data analysis, and/or is there a way to limit the imputed values to be within the minimum and maximum of the actual

Hi all, A short while ago I asked a question about multiple imputation and I got several helpful replies, thanks! I have untill now tried to use the packages mice and norm but both give me errors however. mice does not even run to start with and gives me the following error right away: iter imp variable 1 1 Liquidity.ratioError in chol((v + t(v))/2) : the leading minor of order 1 is not

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'm running da.norm function in R for climate data rngseed(1234567) theta1=da.norm(mydata, thetahat, steps=1000,showits=T) param1=getparam.norm(mydata,theta1) As I understand the 1000 steps represent the markov chain values. Is there a way to plot them? Something like plot(1:1000, param1$mu[]). I just can't find a way to extract them out of my theta1. Thank you, Andrey.

I am conducting a simulation study generating multivariate normal data, deleting observations to create a data set with missing values and then using multiple imputation via da.norm in Schafer's norm package. >From da.norm, I get the following error message: "Error: NA/NaN/Inf in foreign function call (arg 2)" The frequency of the error message seems to depend on the ratio of n

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

I am desperately trying to impute missing data using 'imp.mix' but always run into this yucky error message to which I cannot find the solution. It's the first time I am using mix and I'm trying really hard to understand, but there's just this one step I don't get...perhaps someone knows the answer? Thanks! Jens My code runs:

Dear list, I am trying to impute the two level data, I have a question about a warning. Could you give me some suggestions please? Thank you very much. Here is my code and output of mice package: > ini <- mice(try, maxit=0) > pred=ini$pred > pred FAC1_1 FAC2_1 FAC3_1 FAC4_1 FAC5_1 FAC6_1 FAC7_1 FAC8_1 FAC9_1 FAC10_1 ClassSize_1 ClassSize_2 ClassSize_3 intercept

Hello, I apologize if an answer to my questions is available, or if I submitted this question incorrectly. I have read the mailing lists, as well as the R Project and CRAN homepages. However, I may have missed something. I noticed the package 'norm' has been removed. Its page http://cran.r-project.org/web/packages/norm/index.html now reads: "Package ?norm? was removed from the CRAN

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, 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 <-

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'm trying to impute data using the mi package, but after running through almost the entire first round of imputations (which takes quite a while), it throws this error (I'll include the whole output prior to the error for context). Does anyone know what is causing it, or how I can fix it? More specifically, how can I tell what is throwing the error so I know what to fix? Is

Dear Professor Schaefer I am experiencing a technical difficulty with your mix package. I would appreciate it if you could help me with this problem. When I run the following code, R 2.5.1 and R 2.6.0 crashes. It's been tested on at least 2 windows machine and it is consistent. Execution code it's self was coped from the help file of imp.mix. Only thing I supplied was a fake dataset.

Hello I would like to pass a model formula as an argument to the with.mids function from the mice package. The with.mids functon fits models to multiply imputed data sets. Here's a simple example library(mice) #Create multiple imputations on the nhanes data contained in the mice package. imp <- mice(nahnes) #Fitting a linear model with each imputed data set the regular way works

Greetings folks, Shafer's S-plus package "norm" for multiple imputation of missing values in multivariate normal data has been most kindly and usefully ported to R by Alvaro A. Novo. Shafer's website http://www.stat.psu.edu/~jls/ lists four S-plus packages in all: NORM - multiple imputation of multivariate continuous data CAT - multiple imputation of multivariate

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, I'm using gam in a multiple imputation framework -- specifying the knot locations, and saving the results of multiple models, each of which is fit with slightly different data (because some of it is predicted when missing). In MI, coefficients from multiple models are averaged, as are variance-covariance matrices. VCV's get an additional correction to account for how