similar to: 'matrix' returns integer instead of decimal

I have a simple vector, called tmp that I want to subset based on another vector called vec. Everything works as expected except for below where the subsetting returns something other than the original data. Any ideas? > vec <- c(1,2,3,4,5,59,60,27,32,21) > tmp [1] 1.0 1.1 2.0 2.1 2.2 3.0 3.1 4.0 5.0 5.1 6.0 7.0 8.0 8.1 9.0 [16] 9.1 9.2 10.0 10.1 11.0 12.0 13.0 14.0

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

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

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).

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 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

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-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-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: 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-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