similar to: C-statistic comparison with partially paired datasets

Dear R users, Does anyone know if there's a median filter available in R? I have a considerable amount of images as 256 by 256 matrices and want to smooth them with a 5x5 median filter. Until now I'm not lucky in searching the R-help list/files. Thanks, Hanneke ir. J.M. (Hanneke) Schuurmans (PhD student) Department of Physical Geography Faculty of Geosciences, Utrecht University, The

Dear R-users, I use unlist of POSIX dates to extract the year, hour etc. With that I can search for files in my database which are in the form 'yyyymmddhh_synops.txt' However, I get stucked during midnight where unlist just gives NA's. The script is given below, the problem accurs at acc.period[16] (midnight). However when I write out the character, unlist works well. But

Hello, my test.R file contains two huge arrays (>3000 entries), from which R needs to calculate the Pearson Correlation, if I look at the file the numbers look correct. if I run R R < test.R --no-save I see things like this: 0.723, 0.838, 1.002, 0.364, 0.357, 0.227, 0.982+ , 0.963, 0.535, 1.214, 1.270, 0.832, 1.033, 0.632, 2.482, 1.239, 0.743, 1.077, 0.962, 1.052, 1.075, 1.427, 1.395,

Dear, I wanna to compare AUC generated by two distribution models using the same sample. I tried improveProb function's example code below. set.seed(1) library(survival) x1 <- rnorm(400) x2 <- x1 + rnorm(400) d.time <- rexp(400) + (x1 - min(x1)) cens <- runif(400,.5,2) death <- d.time <= cens d.time <- pmin(d.time, cens) rcorrp.cens(x1, x2, Surv(d.time, death))

Dear R-help, Would it be appropriate to do the following to calculate a p-value for the difference between c-ind of x1 and c-inx of x2 using the output from rcorrp.senc() > r<-rcorrp.senc(x1,x1,y) > pValue<-1-pnorm((r[11]-r[12])/(r[2]/r[5])*1.96) Osman O. Al-Radi, MD, MSc, FRCSC Chief Resident, Cardiac Surgery University of Toronto, Canada

Hi R-users, I'm having some problems in using the Hmisc package. I'm estimating a cox ph model and want to test whether the drop in concordance index due to omitting one covariate is significant. I think (but I'm not sure) here are two ways to do that: 1) predict two cox model (the full model and model without the covariate of interest) and estimate the concordance index (i.e. area

I have two alternative Cox models with C-statistics 0.72 and 0.78. My question is if 0.78 is significantly greater than 0.72. I'm using rcorrp.cens. I cannot find the U statistics in the output of the function. This is the output of the help example: > x1 <- rnorm(400) > x2 <- x1 + rnorm(400) > d.time <- rexp(400) + (x1 - min(x1)) > cens <- runif(400,.5,2) > death

Dear R-users, For a data frame (say in this example X) I want to look up the corresponding value in a 'look-up data frame' (in this example Y). The for-loop works but is very time-consuming because 'X' in reality is very big. Therefore I would like to have a solution with apply. However, I do not succeed. Any suggestions? Thanks in advance, Hanneke

Dear all, I am interested to evaluate reclassification using net reclassification improvement and Integrated Discrimination Index IDI after survival analysis (Cox proportional hazards using stcox). I search a R package or a R code that specifically addresses the categorical NRI for time-to-event data in the presence of censored observation and, if possible, at different follow-up time points. I

Hi, I am trying to calculate net reclassification improvement (NRI) and Inegrated Discrimination Improvement (IDI) for a survival dataset to compare 2 risk models. It seems that the improveProb() in Hmisc package does this only for binary outcome, while rcorrp.cens() does take survival object, but doesn't output NRI or IDI. Can anyone suggest any other packages that can calculate NRI and IDI

Dear R help; I'll preface this by saying that the example I've provided below is pretty long, turgid, and otherwise a deep dive into a series of functions I wrote for a simulation study. It is, however, reproducible and self-contained. I'm trying to do my first simulation study that's quite big, and so I'll say that the output of this simulation as I'd like it to be is

Dear R-listers, I am an MD and clinical epidemiologist developing a measure of comorbidity severity for patients with liver disease. Having developed my comorbidity score as the linear predictor from a Cox regression model I want to compare the discriminative ability of my comorbidity measure with the "old" comorbidity measure, Charlson's Comorbidity Index. I have nearly 10,000

I am reposting my message from April 8th because I never received a response to the original post: Dear R-listers, I am an MD and clinical epidemiologist developing a measure of comorbidity severity for patients with liver disease. Having developed my comorbidity score as the linear predictor from a Cox regression model I want to compare the discriminative ability of my comorbidity measure with

Dear, I wanna to compare AUC generated by two distribution models using the same sample. The AUC for model 1 consists of two columns, column A for 0/1 and column B for probability, eahc with the same row number of 3000. The AUC for model 2 consists of two columns, column A for 0/1 and column B for probability, eahc with the same row number of 10000 rows. I am wondering what value I should put

Dear R-users, I'm experiencing a segmentation fault when using hdf5load(file,load=FALSE). Library(hdf5) loads without problems but when loading a file, R crashes. I compiled R under Unix (Solaris for Sun). There is nothing wrong with the files, as I can run the same script at another place where R runs under Linux. Is it possible it has something to do with the hdf5 libraries where

I am trying to use a new (to me) package (samr) and even when I try to run a very simple example, I get this "cannot open the connection" error. The reason I am writing to r-help rather than to the authors of samr is I think this may be a more general R problem rather than a samr-specific problem. Perhaps something with my installation and write access to some particular place ? I am

Please forgive a stats question. I have to sets of data (unpaired) measured on an ordinal scale. I want to test to see if the two sets are different (i.e. do they have the same location): set1: 1,3,2,2,4,3,3,2,2 set: 4,4,4,3,3,5,4,4 What is the most appropriate non-parametric test to test location? Thanks, John Confidentiality Statement: This email message, including any attachments, is

Hi I have a vector x of length n. I am interested in x[1] being different from the other observations (ie x[-1]). My null hypothesis is that x[1] is drawn from a Gaussian distribution of the same mean as observations x[-1], which are assumed to be iid Gaussian. The (unknown) variance of x[1] is assumed to be the same as the variance of x[-1]. This should be an unpaired t-test. But

There's a funny inconsistency in how t.test handles paired=T or paired=F. If x and y parameters are lists, paired=F works, but paired=T doesn't. > lg=read.csv("my.csv") > a = subset(lg, condition=="a")["score"] > b = subset(lg, condition=="b")["score"] > t.test(a,b) > t.test(a,b, paired=TRUE) Error in `[.data.frame`(y, yok)

Hi, I am using SAM (from siggenes_1.2.11 package) method to select genes from a microarray data set. After installing the latest R2.4.0 on my computer, to my surprise the results are totally different from that calculated using R2.1.1. Even the example code doesn't work the same way under these two versions of R. Does anybody know what is going on? Thanks for any suggestions.