similar to: Correlated ROC curves

Hi, How can I install the server version of rpad on a website. I have read the instructions in http://www.rpad.org/Rpad/ServerNotes.html, but could not figure out how. Do I have to have something like Apache on the website? Is there any instructions in addition to those on http://www.rpad.org/Rpad/. Thanks, - Reza

Hello, In WinBUGS 1.4 manual (http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/manual14.pdf), the gamma density is presented as dgamma(r,mu) where r and mu are the shape and rate parameters, respectively. In JAGS (rjags) manual version 1.0.2, May 9, 2008 (http://www-fis.iarc.fr/~martyn/software/jags/jags_user_manual.pdf), on page 26 the gamma density is presented as dgamma(mu,r) instead of dgamma(r,mu).

Hi, Can anyone tell me why I am not getting the correct intervals for fixed effect terms for the following generalized linear mixed model from HPDinterval: > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252

Hi, I am getting a warning message when I am fitting a generalized linear mixed model (m1.2 below). CHOLMOD warning: matrix not positive definite Error in objective(.par, ...) : Cholmod error `matrix not positive definite' at file:../Supernodal/t_cholmod_super_numeric.c, line 614 Any idea? Thanks for your help, Reza > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32

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

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

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

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

Hi, Dear all R users: Does someone know whether R can calculate the Receiver Operating Characteristic (ROC) Curves? I didn't find it from the packages. Thanks a lot. --- Xin

I might be missing something but I thought that AUC was a measure for comparing ROC curves, so there is nothing else needed to "compare" them. The larger AUC is the higher correlation of 2 variables compared. No other measures or calculations are needed. Jarek Tuszynski -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On

Hello I am trying to make a graph of 10 different lines built each from 4 different segments and to add a darker line that will represent the average of all graphs - all in the same plot.Actually each line is a ROC plot The code I'm using for plotting one line is as follows: logit.roc.plot <- function(r, title="ROC curve") { old.par <- par(no.readonly = TRUE);

Hi, I was trying to draw some ROC curves (prediction of case/control status), but seem to be getting a somewhat jagged plot. Can I do something that would 'smooth' it somewhat? Most roc curves seem to have many incremental changes (in x and y directions), but my plot only has 4 or 5 steps even though there are 22 data points. Should I be doing something differently? How can I provide a

> On Jun 26, 2017, at 11:40 AM, Brian Smith <bsmith030465 at gmail.com> wrote: > > Hi, > > I was trying to draw some ROC curves (prediction of case/control status), > but seem to be getting a somewhat jagged plot. Can I do something that > would 'smooth' it somewhat? Most roc curves seem to have many incremental > changes (in x and y directions), but my plot

Dear all, I try to compare the performances of several parameters to diagnose lameness in dogs. I have several ROC curves from the same dataset. I plotted the ROC curves and calculated AUC with the ROCR package. I would like to compare the AUC. I used the following program I found on R-help archives : From: Bernardo Rangel Tura Date: Thu 16 Dec 2004 - 07:30:37 EST

I did some serach around. It seems that ROC curve computation is not supported on R. Anyone has some leads? Thanks. Jason ===== Jason G. Liao, Ph.D. Division of Biometrics UMDNJ School of Public Health 335 George Street, Suite 2200 New Brunswick, NJ 08903-2688 phone (732) 235-9748, fax (732) 235-9777 http://www.geocities.com/jg_liao __________________________________________________ Great

Dear all, I am looking for an R package that allows me to calculate and plot the confidence intervals for the roc curve using for example some bootstrapping. I tried "ROCR" who seems doing such work but i couldn't find the right option in it. Many thanks Bests Marc _________________________________________________________________ [[alternative HTML version deleted]]

Hallo! Does anybody know a way to calculate the sample size for comparing AUC of ROC curves against 'by chance' with AUC=0.5 (and/or against anothe AUC)? Thanks! Karl

Hi, I don't really understand how ROCR works. Here's another example with a randomforest model: I have the training dataset(bank_training) and testing dataset(bank_testing) and I ran a randomForest as below: bankrf<-randomForest(y~., bank_training, mtry=4, ntree=2, keep.forest=TRUE,importance=TRUE) bankrf.pred<-predict(bankrf, bank_testing)

Hello all, Trying to get this piece of code to work on my data set. It is from http://www.itc.nl/personal/rossiter. logit.roc <- function(model, steps=100) { field.name <- attr(attr(terms(formula(model)), "factors"), "dimnames")[[1]][1] eval(parse(text=paste("tmp <- ", ifelse(class(model$data) == "data.frame", "model$data$",

Hello, I'm a biostatistician in Toronto. I would like to know if there is anything in survival analysis developed in R for the method "Evaluating the Yield of Medical Test" (JAMA. May 14,1982--Vol 247, No.18 Frank E. Harrell, Jr,PhD; Robert M. Califf, MD; David B. Pryor, MD;Kerry L.Lee, PhD; Robert A. Rosait,MD.) Hope to hear from you and thanks Lisa Wang, MSc Project Organiser