similar to: Problems when moving to Imports from Depends

Dear R-help, This is an example in the {Hmisc} manual under rcorr.cens function: > set.seed(1) > x <- round(rnorm(200)) > y <- rnorm(200) > round(rcorr.cens(x, y, outx=F),4) C Index Dxy S.D. n missing uncensored Relevant Pairs Concordant Uncertain 0.4831 -0.0338 0.0462 200.0000

Dear r-helpers! I want to classify my vegetation data with hierachical cluster analysis. My Dataset consist of Abundance-Values (Braun-Blanquet ordinal scale; ranked) for each plant species and relev?. I found a lot of r-packages dealing with cluster analysis, but none of them is able to calculate a distance measure for ranked data. Podani recommends the use of Goodman and Kruskals' Gamma for

Hi R experts, I am trying to do a prognostic model validation study, using cancer survival data. There are 2 data sets - 1500 cases used to develop a nomogram, and another of 800 cases used as an independent validation cohort. I have validated the nomogram in the original data (easy with the Design tools), and then want to show that it also has good results with the independent data using 60

Dear List, I would like to calculate the Goodman-Kruskal gamma for the predicted classes obtained from an ordinal regression model using lrm in the Design package. I couldn't find a way to get gamma for predicted values in Design so have found previous positings suggesting to use : Rcorr.cens(x, S outx = TRUE) in the Hmisc package My question is, will this work for predicted vs

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

Hello R Community, I'm continuing to work through logistic regression (thanks for all the help on score test) and have come up against a new opposition. I'm trying to compute Somers Dyx as some suggest this is the preferred method to Somers Dxy (Demaris, 1992). I have searchered the [R] archieves to no avail for a function or code to compute Dyx (not Dxy). The overview of Hmisc has

Hi, I'm trying to calculate 95% confidence interval of C statistic of logistic regression model using rcorr.cens in rms package. I wrote a brief function for this purpose as the followings; CstatisticCI <- function(x) # x is object of rcorr.cens. { se <- x["S.D."]/sqrt(x["n"]) Low95 <- x["C Index"] - 1.96*se Upper95 <- x["C

Dear all, I am now doing the logistic regression using R. (glm, family=binomial). Besides the standardize summary statistics generated from R, I am also interested in some more informations concerning the model fitting / prediction etc; Particularly I am interested in "pseudo R squar" and "C statistic". I searched the R- help and could only get very limited information. (Post

The Goodman/Kruskal gamma is a nice descriptive rank-order correlation statistic, often used in psychology. It is nice because it is easy to understand. It takes all pairs of values of each variable and asks whether they are congruent (S+ is the number in the same order for both variables) or discordant (S-, opposite ranking). The statistic is (S+ - S-)/(S+ + S-). It is like tau except for the

Dear R-helpers, I recently switched from SAS to R, in order to model the occurrence of rare events through logistic regression. Is there a package available in R to calculate the Goodman-Kruskal Gamma? After searching a bit I found a function "rcorr.cens" which should do the job, but it is not clear to me how to define the input vectors? Is "x" a vector with the fitted

Dear everyone, My question involves the use of the survival object. We can have Surv(time,time2,event, type=, origin = 0) (1) As detailed on p.65 of: http://cran.r-project.org/web/packages/survival/survival.pdf My data (used in my study) is 'right censored' i.e. my variable corresponding to 'event' indicates whether a person is alive (0) or dead (1) at date last seen

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

Does anyone know of an R-function or method to compare two C-statistics (Harrells's C - rcorr.cens) obtained from 2 different models in partially paired datasets (i.e. some similar and some different cases), with one continuous independent variable in each separate model? (in a survival analysis context)? I have noticed that the rcorrp.cens function can be used for paired data. Thanks

Dear all ?Hmisc::rcorr states that it takes as main argument "a numeric matrix". But is it normal that it fails in such an ugly way on a data frame? (See below.) If the function didn't attempt any conversion to a matrix, I would have expected it to state that in the error message that it didn't accept 'data.frame' objects in its input. Also, I vaguely remember having used

System: R 2.3.1 on a Windows XP computer. I am validating several cancer prognostic models that have been published with a large independent dataset. Some of the models report a probability of survival at a specified timepoint, usually at 5 and 10 years. Others report only the linear predictor of the Cox model. I have used Harrell's c index for censored data (rcorr.cens) as a measure of

Hello, I'm trying to install a package that requires a Fortran compiler (Hmisc) using R CMD INSTALL. I downloaded the package source onto my Desktop, unzipped it, and then typed: R CMD INSTALL /Users/brianbeckage/Desktop/Hmisc * Installing *source* package 'Hmisc' ... ** libs g77 -fno-common -g -O2 -c cidxcn.f -o cidxcn.o g77 -fno-common -g -O2 -c cidxcp.f -o cidxcp.o g77

I am running Ubuntu 7.10 and R-2.6.0, and I am having trouble updating packages. There appears to be a problem involving gfortran. For example, here is the output of an attempt to update the Hmisc package. * Installing *source* package 'Hmisc' ... ** libs gfortran -fpic -g -O2 -c cidxcn.f -o cidxcn.o gfortran -fpic -g -O2 -c cidxcp.f -o cidxcp.o gfortran -fpic -g -O2 -c

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

Frank Harrell suggested I re-post with information about the version of R Heres's the information: > > version > _ > platform powerpc-apple-darwin6.8 > arch powerpc > os darwin6.8 > system powerpc, darwin6.8 > status > major 2 > minor 0.1 > year 2004 > month 11 > day 15 > language R Here's what happens

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))