similar to: using rcorr.cens for Goodman Kruskal gamma

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

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-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 list, I have a problem on calculating the standard error of Goodman-Kurskal's gamma using delta method. I exactly follow the method and forumla described in Problem 3.27 of Alan Agresti's Categorical Data Analysis (2nd edition). The data I used is also from the job satisfaction vs. income example from that book. job <- matrix(c(1, 3, 10, 6, 2, 3, 10, 7, 1, 6, 14, 12, 0, 1, 9,

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

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

Hello everyone. This is a question regarding generation of the concordance index (c index) in R using the function rcorr.cens. In particular about interpretation of its direction and form of the 'predictor'. One of the arguments is a "numeric predictor variable" ( presumably this is just a *single* predictor variable). Say this variable takes numeric values.... Am I

I am using the rcorr.cens function from the Hmisc package and the time-dependent ROC curve obtained using tdrocc in the survcomp package. I understand that the C statistic from rcorr.cens has to be subtracted from 1 if high values of the risk variable lower survival. Given that I wonder what the connection is between that C statistic and the AUC from the tdrocc object. If they are substantially

On Wed, 1 Aug 2007, Upasna Sharma <upasna at iitb.ac.in> wrote: > From: "Upasna Sharma" <upasna at iitb.ac.in> > Subject: [R] Goodman Kruskal's tau > > I need to know which package in R calculates the Goodman Kruskal's > tau statistic for nominal data. Also is there any implementation for > multiple classification analysis (Andrews at al 1973) in R?

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 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

Dear List, I am trying to assess the prediction accuracy of an ordinal model fit with LRM in the Design package. I used predict.lrm to predict on an independent dataset and am now attempting to assess the accuracy of these predictions. >From what I have read, the AUC is good for this because it is threshold independent. I obtained the AUC for the fit model output from the c score (c =

Hi I need to know which package in R calculates the Goodman Kruskal's tau statistic for nominal data. Also is there any implementation for multiple classification analysis (Andrews at al 1973) in R? Any information on this would be greatly appreciated. Thank you Upasna -- --------------------------------------------------------------------- Upasna Sharma Research Scholar Shailesh J. Mehta

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

Hi, I try to export the outputs of rcorr into excel. but I got error message,"cannot coerce class "rcorr" into a data.frame". Actually i just need export part of results of this analysis,e.g. p-values or stat-values. Does anyone have sort of exprience before or you can help on how to export subset of results of analysis? Many Thanks! Xin

Hi all, Sorry about posting a really novice question. I was able to run rcorr after converting the list to a matrix by your help. I'm though wondering if there is any way to find out an exact p value as the output only gave me 0 for P value as shown below. I've added options(digits=10), which doesn't seem to help at all. Any help would be appreciated. P D Prime T

Dear list, I get the following message when I use rcorr in library "Hmisc" ------------------------------------------------------ > rcorr(lskPox0t30, type=c("spearman")) Error in "[<-.data.frame"(*tmp*, is.na(x), value = 1e+30) : matrix subscripts not allowed in replacement ------------------------------------------------------ I do not understand

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

lrm does some binning to make the calculations faster. The exact calculation is obtained by running f <- lrm(...) rcorr.cens(predict(f), DA), which results in: C Index Dxy S.D. n missing 0.96814404 0.93628809 0.03808336 32.00000000 0.00000000 uncensored Relevant Pairs Concordant Uncertain 32.00000000

Hello, Please keep this on the list, always cc r-help. One of the files in your attachment is empty: y <- read.csv(file.choose("GT.csv")) Error in read.table(file = file, header = header, sep = sep, quote = quote,? : ? no lines available in input Rui Barradas ? Citando Chaitanya Ganne <Chaitanya.Ganne at jefferson.edu>: > Thank you so much for your input. > > I am