similar to: R crash when using pan for multiple imputation

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

Hello all, I am working on a longitudinal study of children in the UK and trying the PAN package for imputation of missing data, since it fulfils the critical criteria of taking into account individual subject trend over time as well as population trend over time. In order to validate the procedure I have started by deleting some known values ?we have 6 annual measures of height on 300 children

Hi, I've tried to run the multiple imputation for longitudinal data using Pan in R. The trimmed data consist 10 individuals across 4 time points. Following the example in panex.R, I imputated the model with one y variable and only random intercept. It worked well. The next step for me is to imputate a model with one y variable and both random intercept and slope. The program ran well, but

Dear alltogether, I tried pan() to impute NAs for longitudinal data. The terminology in the following output follows the pan manpage. No data are attached to this script as this may be too huge. y = 15 responses pred = at first just intercept was tried (later on covariates should follow) subj = 168 different subjects with 4 to 6 observations for each subject at time points t1, t2, ..., t6 #

Dear all, I'm used to draw bubble plots (gstat) to get a first view of my spatial marked data, but I can't find a way to label legend with marks after having replaced them by a numeric scale. I have a data frame with numeric coords and factors: > names(data) [1] "x" "y" "bloc" "sub" "inoc"

Hey Sean, So the processor does in-memory computing, it reads instructions and operands from the memory array, performs the majority operations within the memory array itself. It does instructions using resistive majority which is AB'+B'C+AC Like it does AND operation as 1: 0, 1, @C; //C=0 2: 0, 1, @Binv; //Binv=0 3: 1, @B, @Binv; //Binv=B 4: @A, @Binv, @C; //C=A.B where each operation is

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

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

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

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

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)"

Hi All! I am desperately needing some help figuring out how to calculate LD50 with a GLMM (probit link) or, more importantly, the standard error of the LD50. I conducted a cold temperature experiment and am trying to assess after how long 50% of the insects had died (I had 3 different instars (non significant fixed effect) and several different blocks (I did 4 replicates at a time)=

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 <-

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 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

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