similar to: Hmisc improveProb() and PredictABEL reclassification () function and continuous NRI

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

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

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 list, I am trying to work out how the improveProb() function works and how to interpret the results, and I have a few questions. I would be grateful if anyone could shed some light on these. in the Net Reclassification Improvement section of the output, is the 2P column the two-sided p-value for the differences in classification? So if a limit is set at 0.05, then lower values indicate

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

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]]

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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 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

Dear useRs, I am trying to import a raster with the line: nlcd <- raster("/home/jpolo/NRI/nlcd_nri5000.tif") And I keep getting an error like this: "Warning message: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,? : ? there is no package called 'rgdal' Error in .rasterObjectFromFile(x, band = band, objecttype =

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.

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

Hi, The message "there is no package called 'rgdal'" means that you don't have rgdal installed. https://cran.r-project.org/web/packages/rgdal/index.html Also, you will get very good patial-centric help if you subscribed to r-sig-geo here https://stat.ethz.ch/mailman/listinfo/r-sig-geo Cheers, Ben > On Sep 6, 2017, at 2:52 PM, john polo <jpolo at mail.usf.edu>

Indeed, Ben, but the question was something more like it is not a Dependency, just Suggested, so why the error... John: If you read the Introduction to the 'raster' package vignette, it indicates that some input formats are supported within the raster package and some rely on other packages. Clearly the attempt to read a TIFF was an example of the latter. Had you used a different input

Hi all, Is the reclassify function useful for really reclassifing ? I couldnt find any samples or documents about this function. I want to pick a packet that''s over a specific rate, and reclassify that to arbitrary class that I specify. I''ve tested with the ingress filter, but it didnt work. (the script below in the ingress, and a simple qdisc, class structure at the egress)

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))

I have ported some R code to C to make it faster. I can perform .Call("foobar",....) once and it works fine. Absolutely correct answer. If I put a loop inside foobar and run the main code routine more than 100 times, it crashes R. Or if I call .Call("foobar"....) seperately more than two tims it crashes R. For the most part I am doing matirx multiplies using EXP