similar to: Missing data analysis in R

R-helpers: Assume that I want to create a series of sequentially named R objects. For example, I might want to call these objects V1, V2, V3 ... V50. To do this, I thought of some sort of looping function like: input.value <- seq(1:50) * 3 for(i in 1:50){ paste("V", i, sep="") <- input.value[i] } Of course this loop will not work since the paste function

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

Many medical journals and publishers require that images, whether photographs or line art, be submitted as high resolution .TIFF images. One option for R users is to produce an image in one format and to convert it to a .TIFF file using a second software program. My experience has been that this option often results in images of poorer quality, often with blurry contours, and a loss of

R-helpers: I am experiencing some odd behavior with the 'matrix' function that I have not experienced before and was wondering if there is something that I was missing in my code. --------------------------------- > sessionInfo() R version 2.6.1 (2007-11-26) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United

R-Experts: Does anyone know if there are R functions to perform the Freeman-Tukey double arcsine transformation and then backtransform it? Thanks, Brant Inman Mayo Clinic

R-experts: I am trying to reproduce some of Paul Allison's results in his little green book on missing data (Sage 2002). The dataset for which I am having problems, "usnews", can be found at: http://www.ats.ucla.edu/stat/books/md/default.htm. I am working on a Windows machine with R 2.5 installed, all packages up-to-date. The problem has to do with the prelim.norm() function of the

Attention R users, especially those that are experienced enough to be opinionated, I need your input. Consider the following simple plot: x <- rnorm(100) y <- rnorm(100) plot(x, y, bty='n') A colleague (and dreaded SAS user) commented that she thought that my plots could be "cleaned up" by connecting the X and Y axes. I know that I can do that with bty='l' but I

R-helpers: I would like to measure the correlation coefficient between the repeated measures of a single variable that is measured over time and is unbalanced. As an example, consider the Orthodont dataset from package nlme, where the model is: fit <- lmer(distance ~ age + (1 | Subject), data=Orthodont) I would like to measure the correlation b/t the variable "distance" at

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

System: R 2.3.1 on a Windows XP computer. I am validating several cancer prognostic models that have been published with a large independent dataset. Some of the models report a probability of survival at a specified timepoint, usually at 5 and 10 years. Others report only the linear predictor of the Cox model. I have used Harrell's c index for censored data (rcorr.cens) as a measure of

R Experts: I am conducting a meta-analysis where the effect measures to be pooled are simple proportions. For example, consider this data from Fleiss/Levin/Paik's Statistical methods for rates and proportions (2003, p189) on smokers: Study N Event P(Event) 1 86 83 0.965 2 93 90 0.968 3 136 129 0.949 4 82 70 0.854 Total

I am running R 2.3.1 on a Windows XP machine. I am trying to import some data into R that is stored as an S-Plus 7.0 .sdd file. When I run the following command, I get this error: > library(foreign) > d <- read.S(file='H:\\Research\\data.sdd') Error in read.S(file = "H:\\Research\\data.sdd") : not an S object The dataset is fairly large, roughly 13000 rows

I am running R 2.3.1 on a Windows XP machine. I am trying to import some data into R that is stored as an S-Plus 7.0 .sdd file. When I run the following command, I get this error: > library(foreign) > d <- read.S(file='H:\\Research\\data.sdd') Error in read.S(file = "H:\\Research\\data.sdd") : not an S object The dataset is fairly large, roughly 13000 rows

R plotting experts: I have a bivariate dataset composed of 300 (x,y) continuous datapoints. 297 of these points are located within the y range of [0,10], while 2 are located at 20 and one at 55. No coding errors, real outliers. When plotting these data with a scatterplot, I obviously have a problem. If I plot the full dataset with ylim = c(0,55), then I cannot see the structure in the data in

Hi all, I'm currently working with a dataset that has quite a few missing values and after some investigation I figured that multiple imputation is probably the best solution to handle the missing data in my case. I found several references to functions in S-Plus that perform multiple imputation (NORM, CAT, MIX, PAN). Does R have corresponding functions? I searched the archives but was not

  I have timeseries data for some factors, and some missing values are there in those factors, I want impute those missing values without disturbing the distribution of that factor, and maintaining the correlation with other factors. Pl. suggest me some imputation methods. I tried some functions in R like aregImpute, transcan. After the imputation I am unable to retrive the data with imputed

R-Experts: I am using the vglm function of the VGAM library to perform proportional odds ordinal logistic regression. The issue that I would like help with concerns the format in which the response variable must be provided for this function to work correctly. Consider the following example: ------ library(VGAM) library(MASS) attach(pneumo) pneumo # Inspect the format of the original dataset

>From Internet I downloaded the file Hmisc.zip and used it for R package updation. and R gave the message 'Hmisc' successfull unpacked. But when I use the functions like aregImpute the package is displaying coundn't find the function Where as in help.search it is giving that use of the function >

I am a new user of R for Windows, enthusiast about the many functions of the Design and Hmisc libraries. I combined the results of a Cox regression model after multiple imputation (of missing values in some covariates). Now I got my vector of coefficients (and of standard errors). My question is: How could I use directly that vector to run programs such as 'nomogram', 'calibrate',

hi, i'm trying to learn how to use aregImpute by doing the examples provided with the package, and after installing Hmisc.1.6-1.zip (for Windows), and running the very first example on R 1.7.1, i get an error message warning me about "mace" (see below) and acepack. i found the acepack package, but its filename ends in tar.gz and i'm finding it difficult to open (because its