similar to: Using mutiply imputed data in NLME

Hi folks, I have five imputed data sets and would like to apply the same recoding routines to each. I could do this sort of thing pretty easily in Stata using MIM, but I've decided to go cold turkey on other stats packages as a incentive for learning more about R. Most of the recoding is for nominal variables, like race, religion, urbanicity, and the like. So, for example, to recode race

Dear All, With kind help from several friends on the list, I am getting close. Now here are something interesting I just realized: for random effects, lmer reports standard deviation instead of standard error! Is there a hidden option that tells lmer to report standard error of random effects, like most other multilevel or mixed modeling software, so that we can say something like "randome

Dear All, I am trying to graph a simple scatter plot where the x axis is year and the y axis is a percentage (percentage of infant death). Instead of plotting the raw data, I want to plot summary statistics such as mean and median. Here is the problem: the value range of y is between 0 and 1, but since infant death is a rare event, the mean and median is very low (something like 5%), which shows

I'm looking for a package that has a start-of-the-art method of imputation of missing values in a data frame with both continuous and factor columns. I've found transcan() in 'Hmisc', which appears to be possibly suited to my needs, but I haven't been able to figure out how to get a new data frame with the imputed values replaced (I don't have Herrell's book). Any

R-users & helpers: I am using Amelia, mitools and cmprsk to fit cumulative incidence curves to multiply imputed datasets. The error message that I get "Error in eval(expr, envir, enclos) : invalid 'envir' argument" occurs when I try to fit models to the 50 imputed datasets using the "with.imputationList" function of mitools. The problem seems to occur

Dear All, When I tried to run the following code (taken from the *spdplyr* package vignettes): library(spdplyr) library(maptools) data(wrld_simpl) worldcorner <- wrld_simpl %>% mutate(lon = coordinates(wrld_simpl)[,1], lat = coordinates(wrld_simpl)[,2]) %>% filter(lat < -20, lon > 60) %>% dplyr::select(NAME) I got the following error messages: Error in (function (cl,

Hey there I am estimating a multilevel model using lmer. I have 5 imputed datasets so I am using mitools to pool the estimates from the 5 > > datasets. Everything seems to work until I try to use > MIcombine to produced pooled estimates. Does anyone have any suggestions? The betas and the standard errors were extracted with no problem so everything seems to work smoothly up until

Dear All, I have been using Sweave (mainly via the Sweave.sh script) and really like it. I am working a paper (using Sweave, of course) which includes several time-consuming computations, and it gets tedious to re-compile the whoel thing every time I made changes. Then I discover the "cacheSweave" package, which seems the right solution to my problem. I only have on problem. Here is

Dear All, Can anyone give me some hints about how to set starting values for a lmer model? For complicated models like this, good starting values can help the numerical computation and make the model converge faster. Thanks! Shige [[alternative HTML version deleted]]

Dear Colleagues, In the following simple ggplot2 code: -------------------------------- m <- ggplot(d.fig, aes(time, prob)) m + stat_summary(fun.data = "median_hilow", conf.int = .95, geom = "smooth") + facet_wrap(~ Cohort, nrow=1) + coord_cartesian(ylim = c(0, .03)) -------------------------------- Is there a way to replace the y-axis label from "prob" to

Hello all, if I have multiple imputed data sets, is there a command or function in R in any package you know of to combine those, I know one common MI approach is rubins rules, is there a way to do this using his rules or others? I know theres ways, like using Amelia from Gary King's website to create the imputed data sets, but how to make them into one or combine them for analysis.

Hello folks, I am a relatively new user of R and created multiply imputed data sets with the 'mice' library. This library provides two functions for complete-data analysis on multiply imputed data set objects (lm.mids and glm.mids). I am trying to estimate a series of Cox PH regression models and cannot figure out the best way to do this. Is it possible with the mitools library?

Dear All, I am trying to compile&install the package "RGtk2" on my Ubuntu 10.04 box. I did not have problem with earlier versions, but with the new version, I got the following error message : ------------------------------------------------------------------------------------------------- * installing *source* package ?RGtk2? ... checking for pkg-config... /usr/bin/pkg-config

Dear All, I am trying to compile&install the package "RGtk2" on my Ubuntu 10.04 box. I did not have problem with earlier versions, but with the new version, I got the following error message : ------------------------------------------------------------------------------------------------- * installing *source* package ?RGtk2? ... checking for pkg-config... /usr/bin/pkg-config

Dear All, Are there R packages that can estimate survival model with long-term survivors? This is sometimes known as "cure" model or "split-population" model. Thanks. Shige [[alternative HTML version deleted]]

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 all, I was wondering whether anyone knows about a program which converts (part of) a data definition file (such as a .dct file for stata, or .sas file for sas) into an R-script. Here is an example with a .sas file: <-- snipp VALUE HTYPE (default=32) 0 = "Not in household" 1 = "Married couple family household" 2 =

Dear All, Is there a way to present results from multiple models in one LaTeX table? I did some google search and found out that xtable cannot automate this process (https://stat.ethz.ch/pipermail/r-help/2006-August/111174.html), are there other alternatives? What about Design and Hmisc? Thanks. Best, Shige

R 2.2.0 windows XP I am beginning to explore the mitools package contributed by Thomas Lumley (thank you Thomas) and I have a few questions: (1) In the examples given in the mitools documentation, the only family argument used is family=binomial. Does the package support family=gaussian and other link functions? I ran the with function with family=gaussian and I obtained results, but I am not

Hi, I have a big dataset which has many missing values and want to implement Multiple imputation via Monte carlo markov chain by following J Schafer's "Analysis of incomplete multivariate data". I don't know where to begin and is looking for a sample R code that implements multiple imputation with EM, MCMC, etc.... Any help / suggestion will be greatly appreciated. David