similar to: Paik, et al., NEJM, 2004, Fig. 4, rate of event at 10 years as a function of covariate

I'm looking for software that makes plots such as fig 4 (a)-(e), fig 5 anf fig 7 of Gray, Robert, "Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis," 1992, J Am Stat Assoc, pp 942-51. In other words, I'm looking for software that takes survival data and a continuous covariate as input and computes a curve giving log hazard

Hi everyone, I am fitting a cox proportional hazard model with a continuous variable "x" as the covariate: fit<-coxph(Surv(time, status)~x) Now I wanted to make a plot of survival probability vs. the covariate, and the 95% confidence interval for the survival probability. It's just like a Kaplan-Meier Survival curve, except now the x axis represents the value of covariate, not

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

Dear list: working with date/times I have come across a problem that ISOdate and ISOdatetime are too slow on large vectors of data. I was surprised just until I looked at the implementation and the man page: "ISOdatetime and ISOdate are convenience wrappers for strptime". In other terms, they convert data to character representation first in order to create a POSIXlt object that is then

Hi, I'm working with timeseries data. The values are every 5 seconds and each series can last up to 4-5 days. To generate the x-axis labels, I'm doing the following: ========================= # Variable for displaying hours on the x-axis rtime <<- as.POSIXct(round(range(timedata), "hours")) # Variable for displaying days on the x-axis stime <<-

Is there some fast (built-in?) way to get the OR of all the elements in a logical vector? In other words, is there some fast (built-in) version of the function vor below? Thanks. -Ben vor <- function(v) { ans <- v[1] if (length(v) > 1) for (i in 2:length(v)) ans <- ans | v[i] ans }

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Dear all, I am using Windows and R 2.9.2 for my analyses. I have a large dataset and I am particularly interested in looking at time to an event for a continuous variable. I would like to produce a plot of log(relative risk) or relative risk (also known as hazard ratio) against the continuous variable. I have spent a long time looking for advice on how to do this but my search has proved

Dear all, I have performed a simple logistic regression using the lrm function from the Design library. Now I want to plot the summary, or make a nomogram. I keep getting a datadist error: options(datadist= m.full ) not created with datadist. I have tried to specify datadist beforhand (although I don't know why it should be done): ddist<-datadist(d) ##where d is my dataset

Hi I got an error message using datadist() from Design package: > library(Design,T) > dd <- datadist(beta.final) > options(datadist="dd") > lrm(Disease ~ gsct+apcct+rarct, x=TRUE, y=TRUE) Error in eval(expr, envir, enclos) : object "Disease" not found All variables inclduing response variable "Disease" are in the data frame

I am running R version 2.4.1 on Windows XP. I have a question regarding the datadist() function from the Design library. I have a data.frame (call it my.data) with 4 columns. When I submit the code datadist(data=my.data) I get the following error message: Error in X[[1]] : subscript out of bounds I suspect there may be something wrong with my data.frame (I'm certain there is nothing

Dear all, I am facing problem with inserting the scheduled day of Observation in the dataset. In the dataset I have only  relative time (table 1) and not scheduled day of observation (day 1, 4, 8, 15, 22, 29, 36, 43). I would appreciate if any one could suggest me how to proceed. Eg: Table 1 The real dataset looks like this with Time, DV ... etc RTime   DV 1        101 4        95 8       

Hi, In the code below, the labels I put on the x-axis are too high (they cross the axis). Can anyone tell me how to move them down? I've tried adj=, padj=, mar=, and various other things, but cannot move them down. Thanks. -Ben labs <- paste('sample', 1:10) plot(1:10, xaxt='n', xlab='') axis(1, at=1:10, labels=labs, padj=1, las=2) # las is a par() parameter

Hello, As an example, I ran the following code: library("rgl") example(plot3d) rgl.snapshot("test.png") The full plot is visible in the window titled RGL device 1 [Focus], but only a small

Hello, As an example, I ran the following code: library("rgl") example(plot3d) rgl.snapshot("test.png") The full plot is visible in the window titled RGL device 1 [Focus], but only a small

I have fitted a logistic regression model > failed.lr2$call lrm(formula = failed ~ Age + task2 + Age:task2, data = time.long, na.action = na.omit) using the Design package functions and would like to generate a nomogram from this model. the datadist information is generated and stored in > ddist time.long$Age time.long$task2 Low:effect 45

Hi, Design isn't strictly an R base package, but maybe someone can explain the following. When lrm is called within a function, it can't find the dataset dd: > library(Design) > age <- rnorm(30, 50, 10) > cholesterol <- rnorm(30, 200, 25) > ch <- cut2(cholesterol, g=5, levels.mean=TRUE) > fit <- function(ch, age) + { + d <- data.frame(ch, age) +

Dear Users, please help with the following DF test: ===== library(tseries) library(timeSeries) Y=c(3519,3803,4332,4251,4661,4811,4448,4451,4343,4067,4001,3934,3652,3768 ,4082,4101,4628,4898,4476,4728,4458,4004,4095,4056,3641,3966,4417,4367 ,4821,5190,4638,4904,4528,4383,4339,4327,3856,4072,4563,4561,4984,5316 ,4843,5383,4889,4681,4466,4463,4217,4322,4779,4988,5383,5591,5322,5404

Hallo! I want to understand / recalculate what is done to get the CI of the logistic regression evaluated with lrm. As far as I came back, my problem is the variance-covariance matrix fit$var of the fit (fit<-lrm(...), fit$var). Here what I found and where I stucked: ----------------- library(Design) # data D<-c(rep("a", 20), rep("b", 20)) V<-0.25*(1:40) V[1]<-25

Hi all R users I did a logistic regression with my binary variable Y (0/1) and 2 explanatory variables. Now I try to draw my nomogram with predictive value. I visited the help of R but I have problem to understand well the example. When I use glm fonction, I have a problem, thus I use lrm. My code is: modele<-lrm(Y~L+P,data=donnee) fun<- function(x) plogis(x-modele$coef[1]+modele$coef[2])