similar to: prelim.norm() function not working

dear list members, there seems to be a problem with the prelim.norm function (package norm) as number of items in the dataset increases. the output of prelim.norm() is a list with different summary statistics, one of them is the missingness indicator matrix "r". it lists all patterns of missing data and a count of how often each pattern occured in the dataset. as the number of items and

Hi everyone, I'm trying to use prelim.norm with a "big" matrix (36 columns by 10000 or so rows). I found that prelim.norm has a built-in limit of 30 columns, but I'd still really like to use it for my data. Does anyone know of a different way to do the same thing? Or, would it be easier to try to modify the source code? Thanks! Josh

Hey eveyone! I hope someone can help wiht this question. I have a matirux of all zeros and ones and I would like to indentify all unique patterns in the rows andthe number of times the pattern occurs. I changed all zeros to NA tried to use prelim.norm to identify all patterns of missing data in the rows. I got the message Warning message: NAs introduced by coercion Any ideas of how

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

Hi, I'm trying to create a stacked bar plot with the satisfaction scores from a customer satisfaction survey. I have results for three stores over several weeks and want to create a weekly graph with a stacked bar for each store. I can flatten the dataframe into a table with absolute frequencies, but I can't find how to get relative frequencies. My dataset looks similar to the example

'sodexquan' is an data frame of numeric variables with some missing values. I would like to approximate these missing values using some algorithm. I thereby chose the mix package. But when trying to run the 'prelim.mix' function (aka the function that will prepare the data for further calculations), R says: prelim.mix(truc,0) Error in as.integer.default(list(c(32, 52, 32, 27, 34,

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

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

Dear alltogether, I experience very strange behavior of imputation of NA's with the NORM library. I use R 2.2.0, win32. The code is below and the same dataset was also tried with MICE and aregImpute() from HMISC _without_ any problem. The problem is as follows: (1) using the whole dataset results in very strange imputations - values far beyond the maximum of the respective column, >

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