similar to: Multiple imputation, especially in rms/Hmisc packages

Hi, I have the following problem- I want to access a list whose elements are imp1, imp2, imp3 etc I tried theusing the paste comand in a for loop see the last for loop below. But I keep calling it df but df = imp1 (for the first run). Any ideas on how I can access the elements of the list? Isaac require(Amelia) library(Amelia) data.use <- read.csv("multiplecarol.CSV", header=T)

Hi, temp3<- read.table(text=" ID CTIME WEIGHT HM001 1223 24.0 HM001 1224 25.2 HM001 1225 23.1 HM001 1226 NA HM001 1227 32.1 HM001 1228 32.4 HM001 1229 1323.2 HM001 1230 27.4 HM001 1231 22.4236 #changed here to test the previous solution ",sep="",header=TRUE,stringsAsFactors=FALSE) ?tempnew<- na.omit(temp3) ?grep("\\d{4}",temp3$WEIGHT) #[1] 7 9 #not correct

Dear R help, I'm having some trouble with model formulas for the ols function in the rms package. I want to have two variables represented as restricted cubic splines, and also include an interaction as a product of linear terms, but I get an error message. library(rms) d <- data.frame(x1 = rnorm(50), x2 = rnorm(50), y = rnorm(50)) ols(y ~ rcs(x1,3) + rcs(x2,3) + x1*x2, data=d) Error in

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

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

I am using -mice- for multiple imputation and would like to use the gelman diagnostic in -coda- to assess the convergence of my imputations. However, gelman.diag requires an mcmc list as input. van Buuren and Groothuis-Oudshoorn (2011) recommend running mice step-by-step to assess convergence (e.g. imp2 <- mice.mids(imp1, maxit = 3, print = FALSE) ) but this creates mids objects. How can I

Hi everyone, I have carried out a multiple imputation in R using Amelia II and have created 5 multiply imputed datasets. The purpose of my research is to fit a Poisson Model to the data to estimate numbers of hospital admissions. Now that I have 5 completed datasets and I have to pool all the 5 datasets to get one combined output for a poisson model. I have checked previous queries about

Hello R-help, Thanks for everyone's very helpful suggestions so far. I am now trying to use aregImpute for my missing data imputation. Here are the code and error messages. Any suggestions would be very much appreciated. Sincerely, Anders Corr ######################################## #Question for R-Help on aregImpute ######################################## #DOWNLOAD DATA (61Kb)

Hello, I am using R.12.2.0. I am trying to generate bootstrap confidence intervals using bootcov from the rms package. I am able to impute the missing data using aregImpute and to perform a linear regression on the imputed datasets using fit.mult.impute, but I am unable to use bootcov to generate the confidence intervals for the R-squared. Here is a small example that should duplicate the

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

On Aug 13, 2006, at 11:16 PM, Fernando Magno Quintao Pereira wrote: > > Dear LLVMers, > > I am trying to fold memory operands in the way that is done in > RegAllocLocal.cpp, or in LiveIntervalAnalysis.cpp, but I am getting > errors > that > I don't know how to fix. Could someone tell me which steps should I > take > in order > to correctly fold memory

Dear LLVMers, I am trying to fold memory operands in the way that is done in RegAllocLocal.cpp, or in LiveIntervalAnalysis.cpp, but I am getting errors that I don't know how to fix. Could someone tell me which steps should I take in order to correctly fold memory operands? The code that I am using is: const TargetMachine & target_machine = this->machine_function->getTarget();

> Hi Fernando, > > It's hard to say exactly what's happening because I don't know your > code (though, from the stack trace, it seems like there's some sort > of memory debacle), but looking at the comment in the > LiveVariableAnalysis.cpp file where it's folding memory operands, it > might explain somethings better: > > // Folding the

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

Hi all, I'm reposting this with a more appropriate subject. Do I need to define limits as the error message seems to suggest? If so, how? The error message, my code, the output and the first few lines of my data are all below. Thank you! "Error in Getlim(at, allow.null = TRUE, need.all = TRUE) : variable dmodel.df does not have limits defined in fit or with datadist" My code:

Dear R-Users, The following question is more of general nature than a merely technical one. Nevertheless I hope someone get me some answers. I have been using the mice package to perform the multiple imputations. So far, everything works fine with the standard regressions analysis. However, I am wondering, if it is theoretically correct to perform nonparametric models (GAM, spline

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

Dear R users, I am using the MICE package. Specifically, at some point in my code I have imp2=mice(PoptotalMICE,imputationMethod="logreg2") And R returns... iter imp variable 1 1 MICEYError in logitreg(xobs, yobs, intercept=F) : couldn't find function "ms" I have been looking for this ms function on the web, hoping it was just a matter of downloading a

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

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 <-