similar to: Opinion on R plots: connecting X and Y

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

I am looking for a book that discusses the theory of multiple imputation (and other methods of dealing with missing data) and, just as importantly, how to implement these methods in R or S-Plus. Ideally, the book would have a structure similar to Faraway (Regression), Pinheiro&Bates (Mixed Effects) and Wood (GAMs) and would be very modern (i.e. published within the last couple of years).

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

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

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

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: 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

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

On July 12, 2004 Spencer Graves wrote an email describing essentially the same issue that I would like help on: calling the confint function from within another homemade function. Because he provided many good examples of the problem, I will not reproduce them here but will instead refer readers to the original posting: http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg23826.html It is

R-helpers, Below is a simple example of some output that I am getting while trying to work with a data frame in R 2.12.1 for Mac. ----- > testdat <- data.frame(matrix(ncol=10, nrow=10)) > colnames(testdat) <- c('a','b','c','d','e','f','g','h','i','j') > testdat[seq(1,10,3),] <-

R-helpers, I have had difficulty installing the "pcvsuite" package on R version 2.12.0 (2010-10-15). The pcvsuite package is not available on CRAN, but is located for download at the following website at the University of Washington: Windows version http://labs.fhcrc.org/pepe/dabs/pcvsuite_1.0.zip Mac version

R-Helpers: I would like to perform sample size calculations for an experiment. As part of this process, I would like to know how various assumptions affect the sample size calculation. For instance, one thing that I would like to know is how the calculated sample size changes as I vary the difference that I would like to detect. I tried the following first, but got the associated error

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

This email is intended to highlight 2 problems that I encountered running R 2.5.0 alpha on a Windows XP machine. #1 - Open script error If I click the "Open folder" icon on the toolbar, R opens my script files perfectly. However, when I select "File > Open Script > MyFileLocation", I get a fatal error that causes R to close immediately. This error was reproduced on 3

R-helpers, I recently submitted a help request for the predict.drm function found in the drc package. I am still having issues with the function and I am submitting reproducible code hoping that somebody can help me figure out what is going on. -------- library(drc) # Fit a 4 parameter logistic model to ryegrass dataset fit <- drm(rootl ~ conc, data = ryegrass, fct = LL.4()) summary(fit) #

System: R 2.3.1 on Windows XP machine. I am building a logistic regression model for a sample of 100 cases in dataframe "d", in which there are 3 binary covariates: x1, x2 and x3. ---------------- > summary(d) y x1 x2 x3 0:54 0:50 0:64 0:78 1:46 1:50 1:36 1:22 > fit <- glm(y ~ x1 + x2 + x3, data=d, family=binomial(link=logit)) >

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