similar to: Hazard plot

Dear All, Sorry to bother you again. I have a model: coxfita=coxph(Surv(rem.Remtime/365,rem.Rcens)~all.sex,data=nearma) and I'm trying to do a plot of Schoenfeld residuals using the code: plot(cox.zph(coxfita)) abline(h=0,lty=3) The error message I get is: Error in plot.window(...) : need finite 'ylim' values In addition: Warning messages: 1: In sqrt(x$var[i, i] * seval) : NaNs

Dear All, I am attempting to use forward and/or backward selection to determine the best model for the variables I have. Unfortunately, because I am dealing with patients and every patient is receiving treatment I need to force the variable for treatment into the model. Is there a way to do this using R? (Additionally, the model is stratified by randomisation period). I know that SAS can be

Dear R-Help, I am using the 'mfp' package. It produces three plots (as I am using the Cox model) simultaneously which can be viewed together using the following code: fit <- mfp(Surv(rem.Remtime,rem.Rcens)~fp(age)+strata(rpa),family=cox,data=nearma,select=0.05,verbose=TRUE) par(mfrow=c(2,2)) plot(fit) They can be viewed separately but the return key must be pressed after each graph

-- begin included message ----- I am looking at a continuous variable, age. I am looking at time to 12-month remission and can calculate the HR and 95% confidence interval are follows: coxfita = coxph(Surv(rem.Remtime,rem.Rcens)~nearma$all.age,data=nearma) exp(coxfita$coefficients) exp(confint(coxfita)) However, because I am looking at age as a continuous variable I cannot draw a Kaplan-Meier

Hi all, I'm very confused! I've been using the same code for many weeks without any bother for various covariates. I'm now looking at another covaraite and whenever I run the code you can see below I get an error message: "Error in rep(0, nrow(data)) : invalid 'times' argument" This code works: # remove 'missing' cases from data # snearma <-

Dear All, I am contemplating centering the covariates in my Cox model to reduce multicollinearity between the predictors and the interaction term and to render a more meaningful interpretation of the regression coefficient. Suppose I have two indicator variables, x1 and x2 which represent age categories (x1 is patients less than 16 while x2 is for patients older than 65). If I use the following

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 R-help, I am using R 2.14.1 on Windows 7. I would like to produce a plot like the attached - although simplified to actual vs. Predicted survival time with distinguishing marks for censored and observed points. I have a dataset and have fitted a Cox model to it. In an attempt to visualise how accurate the model is it would be ideal if I could plot the actual survival times against the

Hello, I have a problem regarding calculation of Cumulative Incidence Function. The event of interest is failure of bone-marrow transplantation, which may occur due to relapse or death in remission. The data set that I have consists of- lifetime variable, two indicator variables-one for relapse and one for death in remission, and the other variables are donor type (having 3 categories), disease

Dear R-help, I am using R 2.14.1 on Windows 7 with the 'gfcure' package (cure rate model). I have included the treatment variable in the cure part of the model as shown below: Ø ref_treat <- gfcure(Surv(rem.Remtime,rem.Rcens)~1,~1+strata(drpa)+factor(treat(delcure)),data=delcure,dist="loglogistic") >From that I can obtain the coefficients, standard errors etc as per

Hi, at my local university network, I have to make sure no student uploads more than x GB/day. So far, I give them unlimited bandwidth until they have more than y GB (y<x) upload. After that, I use the u32 filter to associate the affected IP address with a HTB class limited to the rate (remainingtraffic/remainingtime). Since the accounting is done with ipt_ACCOUNT right now and the netfilter

Dear All, I have analysed time to event data for continuous variables by considering the multivariable fractional polynomial (MFP) model and comparing this to the untransformed and log transformed model to determine which transformation, if any, is best. This was possible as the Cox model was the underlying model. However, I am now at the situation where the assumption that the competing risks

V; Sent via BlackBerry from T-Mobile -----Original Message----- From: r-help-request at r-project.org Date: Tue, 30 Sep 2008 12:00:06 To: <r-help at r-project.org> Subject: R-help Digest, Vol 67, Issue 31 Send R-help mailing list submissions to r-help at r-project.org To subscribe or unsubscribe via the World Wide Web, visit https://stat.ethz.ch/mailman/listinfo/r-help or, via email,

Dear all, I would like to draw three forest plots to represent results at years 1, 2 and 3. I have the data as point estimates and 95% confidence intervals. Using the following code I can get three basic forest plots - the first which has the table of results. I have to plot each separately as the usual par(mfrow=c(3,1)) does not work with the function forestplot within rmeta. I can easily put

Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <-

Fixed 'maxiter' in the help file. Thanks. Please give the original source of that dataset. That dataset is a tiny sample of GUSTO-I and not large enough to fit this model very reliably. A nomogram using the full dataset (not publicly available to my knowledge) is already available in http://biostat.mc.vanderbilt.edu/tmp/bbr.pdf Use lrm, not lrm.fit for this. Adding maxit=20 will

Dear all, I am using R version 2.9.2 in Windows. I would like to output the results of a function I have written to a .txt file. I know that I can do this by using the code write.table(boothd(10),"boothd10.txt",sep="\t",append=TRUE) etc. However, I would like to bootstrap my function 'boothd' several times and get each vector of results as a new line in my text

> On Sep 14, 2017, at 12:30 AM, Bonnett, Laura <L.J.Bonnett at liverpool.ac.uk> wrote: > > Dear all, > > I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk > > I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have

Hi all, I have this microarray large microarray data set (ALL) from which I would like to subset or extract a set of data based on a factor ($mol.biol). I looked up some example of subsetting in, picked up two commands and tried both but I got error messages as follows > testset <- subset(ALL, ALL$mol.biol %in% c("BCR/ABL","ALL1/AF4")) >> Error in

Hi Everyone, I have data in a long format e.g. there is one row per patient but each follow-up appointment is included in the row. So, a snippet of the data looks like this: TrialNo Drug Sex Rand Adate1 Date1 Dose1 Time1 Adate2 Date2 Dose2 Time2 B1001029 LTG M 15719 30/04/2003 15825 150 106 29/08/2003 15946 200 227 B1117003 LTG M 15734 30/04/2003 15825 200 91 03/09/2003 15951 250 217