similar to: Hmisc improveProb() function

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 Sirs. I am working with the R packages Hmisc and PredictABEL to make NRI estimates from my Cox models with and without a specific biomarker. According to Pencina et al (Statistics in Medicine 2010, DOI: 0.1002/sim.4085 ), a continuous/non-categorical NRI (NRI>0) is to be used when there are no obvious reason to categorize risk, such as the risk of future cardiovascular events in patients

Hello Thanks to Frank Harrell for the great Design package. I noted from Pencina's Article (Statistics in Medicine Vol 27, pp157-172) that the result obtained for the Net Reclassification Index depends upon categorical cut-offs for risk (eg in the paper <6%, 6-20% and >20%). I wondered how the improveprob function arrives at its cut-offs, and whether there was any function to change the

As describe in the Hmisc package's improveProb function, that function is for binary Y. And it's best to use category-free methods. Frank Petergodsk wrote > Hi > > I'm new to R. > > Is it possible to use the improveProb function to generate categorybased > NRI using a Cox model? > I believe I saw someone mentioning the possibility, but I can't find the

Greetings, I have generated several ROC curves and would like to compare the AUCs. The data are cross sectional and the outcomes are binary. I am testing which of several models provide the best discrimination. Would it be most appropriate to report AUC with 95% CI's? I have been looking in to the "net reclassification improvement" (see below for reference) but thus far I can only

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

Dear all I am trying to compare the performances of several methods using the AUC0.1 and not the whole AUC. (meaning I wanted to compare to AUC's whose x axis only goes to 0.1 not 1) I came to know about the Mcneil Hanley test from Bernardo Rangel Tura and I referred to the original paper for the calculation of "r" which is an argument of the function cROC. I can only find the

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 users Function improveProb in the rms library calculate NRI and IDI for predictions of binary outcome. Do anyone extent its use in survival data? Many thanks. *Yao Zhu* *Department of Urology Fudan University Shanghai Cancer Center Shanghai, China* [[alternative HTML version deleted]]

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

Hi, I would like to compare differences in AUC from 2 different models, glm and gam for predicting presence / absence. I know that in theory the model with a higher AUC is better, but what I am interested in is if statistically the increase in AUC from the glm model to the gam model is significant. I also read quite extensive discussions on the list about ROC and AUC but I still didn't find

Dear all I have used the lda and predict functions to classify a set of objects of unknown origin. I would like to use a jackknife reclassification to assess the degree to which the outcomes deviate from that expected by chance. However, I can't find any function that allows me to do this. Any suggestions of how to generate the jackknife reclassification to assess classification accuracy?

Dear all, here is a example of my problem: /#data# g<-c(1,1,1,2,2,2) A<-runif(6,min=1,max=5) B<-runif(6,min=100,max=1000) C<-runif(6,min=30,max=31) D<-runif(6,min=67,max=98765) var<-cbind(A,B,C,D) label<-colnames(var) store<-data.frame(matrix(ncol=2)) colnames(store)=c("usedVar","prediction") library(MASS)#get lda for (i in c(1:4)) { for (k in

Hi! I replace some elements of a matrix a > a [,1] [,2] [,3] [1,] 1 2 3 [2,] 4 5 6 [3,] 7 8 9 [4,] 10 11 12 according to a reclassification matrix such > pares [,1] [,2] [1,] 1 2 [2,] 5 6 [3,] 8 7 to get > b [,1] [,2] [,3] [1,] 1 2 3 [2,] 4 5 6 [3,] 7 8 9 [4,] 10 11 12 As both a and

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 All, I am using a specific approach for my master thesis. In essence, a supervised reclassification is used as an intermediate step to find chemical parameters which are able to reclassify defined groups. These variables will be used in a next step where location and scale estimators of the groups are important. Traditionally linear discriminant analysis is used for reclassification which

Hi Colton, You could divide your write task into chunks that do not violate the 2^31-1 limit. write.table has an append argument (default FALSE). Figure out a row chunk size nri < nr such that nri * nc is under 2^31-1 and use write.table() to write that out. Then use write.table( append = TRUE, ) for the next chunk of rows, looping over chunks until done. Two chunks will get your 2.8

Hi Steven, Yes, both file systems are the same. rsync -nri --modify-window=1 <src> <dest> Gives me the following for most files >f..T....... 2015_167_1_1__Boy_What_A_Girl_R2/2015_167_1_1__Boy_What_A_Girl__UHD_DPX_R2/ BWAG_R2_00138428.dpx Although a few have >f..T......n 2015_167_1_1__Boy_What_A_Girl_R2/2015_167_1_1__Boy_What_A_Girl__UHD_DPX_R2/ BWAG_R2_00135909.dpx

Hi all, I've installed rsync-2.5.6 on a sun box (solaris 8). When I run rsync using the command rsync -a ssh /opt/local/pkgs/httpd/htdocs/ root@remotemachine:/mnt/ I get the error rsync: connection unexpectedly closed (0 bytes read so far) rsync error: error in rsync protocol data stream (code 12) at io.c(165) What could be the reason? Any help would be appreciated. Thanks, Rajesh.

I'm trying to do a rewrite of the dspam_plugin for dovecot 1.1b1. There are some API changes that warranted an update of the plugin. Also, I wanted the dspam_plugin to be able to handle pristine mails for dspam retraining, as opposed to the signature based retraining. Question: How can I retrieve the full unix path for a specific mail? The original code uses mail_get_first_header() to