similar to: Bug in printing symbolic derivative (PR#1697)

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 posted this message earlier in "Rmetrics" and I don't know whether I posted in the wrong place, so I'm posting it again in Rhelp. I have a function in x and y and let's call it f(x,y). I need to get the Hessian matrix. i.e I need (d^2f/dx^2), (d^2f/dxdy), (d^2f/dydx), (d^2f/dy^2).I can get these using the D function. now I need to evaluste the hessian matrix for

I have two alternative Cox models with C-statistics 0.72 and 0.78. My question is if 0.78 is significantly greater than 0.72. I'm using rcorrp.cens. I cannot find the U statistics in the output of the function. This is the output of the help example: > x1 <- rnorm(400) > x2 <- x1 + rnorm(400) > d.time <- rexp(400) + (x1 - min(x1)) > cens <- runif(400,.5,2) > death

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

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

Dear R-help, I am having a problem with the interpretation of result from validate.cph in the Design package. My purpose is to fit a cox model and validate the Somer's Dxy. I used the hypothetical data given in the help manual with modification to the cox model fit. My research problem is very similar to this example. This is the model without stratification: > library(Design) > f1

I am doing some coxPH model fitting and would like to have some idea about how good the fits are. Someone suggested to use Frank Harrell's C-index measure. As I understand it, a C-index > 0.5 indicates a useful model. I am probably making an error here because I am getting values less than 0.5 on real datasets. Can someone tell me where I am going wrong please ? Here is an example using

Dear R users I use Windows XP, R2.5.1 (I have read the posting guide, I have contacted the package maintainer first, it is not homework). In a research project on renal cell carcinoma we want to compute Harrell's c index, with optimism correction, for a multivariate Cox regression and also for some univariate Cox models. For some of these univariate models I have encountered an error

Hello, I discovered SAGA, an interesting free GIS, a few days ago and now, I would like to use it from within R 2.6.2 using the RSAGA package. I read the documentation for this package and thought that I understood it correctly for trying to call some of the SAGA modules. For getting the information on the usage of and arguments required by the SAGA command line "Import Binary Raw

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

Hello All, as I?m new to R and survival analysis, I?ve got a question about the Design::validate function: My Code: cox <- cph(Surv(t,status) ~ var1 + var2 + var3, data=data, x=TRUE, y=TRUE, surv=TRUE) cox.val <- validate(cox, B=10, dxy=TRUE, pr=TRUE); My output (cox.val): index.orig training test Dxy -0.3639222921368090891

Dear Sir or Madam? I am a doctor of urology,and I am engaged in developing a nomogram of bladder cancer. May I ask for your help on below issue? I set up a dataset which include 317 cases. I got the Binary Logistic Regression model by SPSS.And then I try to reconstruct the model ?lrm(RECU~Complication+T.Num+T.Grade+Year+TS)? by R-Project,and try to internal validate the model through

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'),

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

Sorry I have a very simple question: I used somers2 function from Design package: > z<- somers2(x,y, weights=w) results are: >z C Dxy n Missing 0.88 0.76 500 0.00 Now I want to call only the value of C to be used in further analyses, but I fail to do it. I have tried: > z$C NULL > z[,C] Error in z[,C]: incorrect number of dimensions and some other silly

ioemu: fix SDL mouse events processing - GetRelativeMouseState always returns the last position, so when the polling loop gets several mouse events in one go, we would send useless ''no move'' events. - So as to make sure we don''t miss any mouse click / double click, we should not use GetRelativeMouseState() to get the button state, but keep records of the button

Thank you so much to Mark and Gabor for their codes (please see below). Now I solved my problem : ) Best, Jorge # --------- Mark's code res=sapply(colnames(dx), function(.colname) { if (.colname %in% colnames(dy)) dy[,.colname] else {numeric(nrow(dy))} }) res # --------- Gabor's code res=as.matrix(replace(as.data.frame(0 * dx), colnames(dy), dy)) # If dx were a data frame

I want to implement the following algorithm in R: I want to split my data, use a t test to compare both means of the groups to see if they significantly differ from each other. If this is a yes (p < alpha) I want to split again (into 4 groups) and do the same procedure twice, and stop otherwise (here the problem arises). As a final result I would have different groups of data. I made some

When legend() is used with the angle argument as follows, not only the boxes beside the legend text, but also the whole legend box is filled with shading lines. I think this is not intended: plot(1:10) legend(8, 4, c("A", "B"), angle=c(10, 80), fill=NULL, density=20) I'd suggest as a fix (legend.R of R-1.5.0): 25c25 < rect2 <- function(left, top, dx, dy,