similar to: confidence interval

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

Hi Does anyone know whether the rms package provides a confidence interval for the bootstrap-corrected Dxy or c-index? I have fitted a logistic model, and would like to obtain the 95% confidence interval of the bootstrap-corrected area under the ROC curve estimate. Thanks. [[alternative HTML version deleted]]

Dear R-philes, I am plotting ROC curves for several cross-validation runs of a classifier (using the function below). In addition to the average AUC, I am interested in obtaining a confidence interval for the average AUC. Is there a straightforward way to do this via the ROCR package? plot_roc_curve <- function(roc.dat, plt.title) { #print(str(vowel.ROC)) pred <-

Hello Wise Ones... I need a clever way around a problem with findInterval. Consider: vec1 <- 1:10 vec2 <- seq(1, 10, by = 0.1) x1 <- c(2:3) a1 <- findInterval(x1, vec1); a1 # example 1 a2 <- findInterval(x1, vec2); a2 # example 2 In the problem I'm working on, vec* may be either integer or numeric, like vec1 and vec2. I need to remove one or more sections of this vector;

Dear All, I am trying to calculate a 95% confidence interval for the difference in two c statistics (or equivalently D statistics). In Stata I gather that this can be done using the lincom command. Is there anything similar in R? As you can see below I have two datasets (that are actually two independent subsets of the same data) and the respective c statistics for the variables in both cases.

Dear All, Apologies if you have a seen a question like this from me before. I am hoping that if I re-word my question more carefully someone may be able to offer more help than the last time I asked something similar. I am using R 2.9.2 and Windows XP. I am trying to determine if there is a statistically significant difference between two c-statistics (or equivalently D statistics). In Stata

I am writing a function where the arguments are names of objects or variable names in a data frame. To convert the strings to the objects I am using eval(parse(text=name)): f.graph.two.vbs<-function(dataname,v1){ val<-paste(dataname,v1,sep="$") val<-eval(parse(text=val)) val } However running this returns an error:

Hi Group, I have a question about obtaining the bias-corrected c-index using validate from the Design library. As an example, consider the example from help page: library(Design) ?validate.lrm n <- 1000 age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'),

I have an R script with the following applicable lines: xshort <- window(s, start=st, end=ed) . . . xshort <- ts(xshort, frequency=1, start=1) . . . m1 <- m2 <- m3 <- m4 <- m5 <- m6 <- NULL m1 <- tslm(xshort ~ trend) I get an error: Error in get(dataname) : object 'xshort' not found When I do traceback() I get: 3: get(dataname) 2: tslm(xshort ~

Hey everybody, i am using the rugarch-package and its great! I have a pretty easy problem, but i just dont get it, so thanks if you can help me. Normally i use: / data(DATANAME) spec = ugarchspec() fit = ugarchfit(data = x[,1], spec = spec) fit slotNames(fit) names(fit at fit) coef(fit) infocriteria(fit) likelihood(fit) nyblom(fit) signbias(fit) head(as.data.frame(fit)) head(sigma(fit))

At 10:37 PM 5/1/00 -0400, E. S. Venkatraman wrote: >I have the following problem. I have survival data (time, status) along >with several covariates (X1, X2,..., Xn). I want to fit a Cox model for >each of the covariate (univariately) and obtain the fitted probability of >survival at a fixed time point t0 and covariate value Xi0. I tried to do >this in a for loop where the index

Hi, I have a matrix of X and Y coordinates, and one pair of coordinates that I need to know it corresponds (or is closest) to which row in the matrix. For example: MC<- cbind(rep(1:1000,each=1000),rep(1:1000,1000)); p<-c(543.1,440.05); I know that if you do which.min you'll get the answer, which will be row # 542440, but which.min is slow and this process needs to be repeated a lot. I

I have been using R the past couple of years to run models on data we are collecting. I recently got a new computer and updated to a new version of R (2.60 -> 2.90). Since the update, I cannot get my syntax to run. I have tried copying the file it is looking for into many different directories to try and run it. In the last version, I found that it was easiest if the file was copied into the R

Hello I have created a graph using the following commands: <<< startBReP3O1T <- diffs$BReP3O1T - diffs$diff_BReP3O1T endBReP3O1T <- diffs$BReP3O1T x <- seq(47,89, length = 10) ymin <- min(min(startBReP3O1T), min(endBReP3O1T)) ymax <- max(max(startBReP3O1T), max(endBReP3O1T)) y <- seq(ymin, ymax, length = 10) plot(x,y, type = 'n', xlab = 'Age', ylab =

Hi R-users! Being new to R, and a fairly advanced Stata-user, I guess part of my problem is that my mindset (and probably my language as well) is wrong. Anyway, I have what I guess is a rather simple problem, that I now without success spent days trying to solve. I have a bunch of datasets imported from Stata that is labelled aa_2000 aa_2001 aa_2002, etc. Each dataset is imported as a matrix, and

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 to everybody! I´m just a beginner in R, and I´m trying to replace values in a distance matrix with a concret condition: replace all values (elements) lower than 4.5 with value=18. I´ve tried this, but it doesn´t work... Dxy would be my 117 x 117 euclidean distance matrix M18 and M4.5 would be 117 x 117 matrices: M18<-matrix(rep(18,13689),nrow=117)

Dear R help My problem is very similar to the analysis detailed here. If we use the mayo dataset provided with the survivalROC package the estimate for Somer's Dxy is very negative -0.56. The Nagelkerke R2 is positive though 0.32. I know there is a difference between explained variation and predictive ability but I am surprised there is usch a difference given that even a non predictive model

The following single line of code shows what I am trying to do, and the problem I am having... barplot(c(101,102,103),ylim=c(100,103)) The 'xaxis' is missing, and the grey bars 'fall off' the plot area. This is generally ugly, and I would like to trim the bars (ideally they would have a ragged appearance to show that I am 'zooming in'). I can see why what I am trying to

Dear professor, I am currently using Design package and the cph formula for assessing multivariable analysis. I am tryng to get the C-index for my survival model based on Dxy coefficient. I am confused since there is a negative value. Do I need to used the absolute Dxy ? index.orig training test optimism index.corrected n Dxy -0.341357727 -0.344002740