similar to: problem with abline

Hi, I would like to write a function which has the following syntax: myfn <- function(formula, ftime, fstatus, data) { # step 1: obtain terms in `formula' from dataframe `data' # step 2: obtain ftime from `data' # step 3: obtain fstatus from `data' # step 4: do model estimation # step 5: return results } The user would call this function as: myfn(formula=myform,

Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10)

Hello everyone, (This is my second question posted today on the R list). I am carrying out a competing risks analysis using the cuminc function...this takes the form: cuminc(ftime,fstatus,group) In my study, fstatus has 3 different causes of failure (1,2,3) there are also censored cases (0). "group" has two levels (0 and 1). I therefore have 6 different cumulative incidence curves:

Hi all I do not understand why I am getting the following error message. Can anybody help me with this? Thanks in advance. install.packages("cmprsk") library(cmprsk) result1 <-crr(ftime, fstatus, cov1, failcode=1, cencode=0 ) one.pout1 = predict(result1,cov1,X=cbind(1,one.z1,one.z2)) predict.crr(result1,cov1,X=cbind(1,one.z1,one.z2)) Error: could not find function

Hi I am a newbie to R but have tried a number of ways in R to do this and can't find a good solution. (I could do it out of R in perl or awk but would like to know how to do this in R). I have a large data frame 49 variables and 7000 observations however for simplicity I can express it in the following data frame Base, Image, LVEF, ES_Time A, 1, 4.32, 0.89 A, 2, 4.98, 0.67 A, 3, 3.7, 0.5

Dear R-help, I'm very sorry to ask 2 questions in a week. I am using the package 'crr' and it does exactly what I need it to when I use the dataset a. However, when I use dataset b I get the following error message: Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number =

Dear all, I am trying to use the crr function in the cmprsk package version 2.2 to analyse 198 observations.I have receive the error in solve.default. Can anyone give me some insights into where the problem is? Thanks here is my script : cov=cbind(x1,x2) z<-crr(ftime,fstatus,cov)) and data file: x1 x2 fstatus ftime 0 .02 1 263 0 .03 1 113 0 .03 1 523

Hi all, I am trying to fit t distribution using the function "tFit" in the library(fBasics). I am using the code tFit(datac[[2]]) and it returns the following list. Title: Student-t Parameter Estimation Call: tFit(x = datac[[2]]) Model: Student-t Distribution Estimated Parameter(s): df 78.4428 I just wonder how can I refer to the estimated parameters. I tried

Hi there! I would like to ask kindly for some further information concerning Rsync software. What I am planning to do is to replicate data (database) at certain intervals. My first question is, can I do this with Rsync? To be more specific I have a mysql database at a point and I want this database to be replicated to a remote side at real-time. In other words I need to setup a backup

Dear R-user: I am using survreg(Surv()) for fitting a Tobit model of left-censored longitudinal data. For logarithmic transformation of y data, I am trying use survreg.distributions in the following way: tfit=survreg(Surv(y, y>=-5, type="left")~x + cluster(id), dist="gaussian", data=y.data, scale=0, weights=w) my.gaussian<-survreg.distributions$gaussian

Hi. I am looking for a function for left-truncated data. I have one data set with 2 variables (Hours~Yrs_Ed). I already left-censored the data at 200 and left-truncated it at the same spot, so that I am able to make 2 estimations (one for censoring and one for truncation). I know how to make the linear regression for the left-censored variable (hours) and how to plot the regression line into the

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

Folks, Based on http://www.biostat.wustl.edu/archives/html/s-news/1999-06/msg00125.html I thought I should experiment with using survreg() to estimate tobit models. I start by simulating a data frame with 100 observations from a tobit model > x1 <- runif(100) > x2 <- runif(100)*3 > ystar <- 2 + 3*x1 - 4*x2 + rnorm(100)*2 > y <- ystar > censored <- ystar <= 0

Calum had a long question about drawing survival curves after fitting a Weibull model, using pweibull, which I have not reproduced. It is easier to get survival curves using the predict function. Here is a simple example: > library(survival) > tfit <- survreg(Surv(time, status) ~ factor(ph.ecog), data=lung) > table(lung$ph.ecog) 0 1 2 3 <NA> 63 113 50 1

Dear R-help list members, I am trying to create my own axes when plotting a cumulative incidence curve using the plot.cuminc function in the CMPRSK library. The default x-axis places tick marks and labels at 0, 20, 40, 60, and 80 (my data has an upper limit of 96), whereas I want them at my own specified locations. Here is my example code: library(cmprsk) attach(MYDATA) MYCUMINC <-

The number of recent questions from umn.edu makes me wonder if there's homework involved.... Simpler for your example is to use get and subset. dat <- structure(..... as found below var.to.test <- names(dat)[4:6] #variables of interest nvar <- length(var.to.test) chisq <- double(nvar) for (i in 1:nvar) { tfit <- survdiff(Surv(time, completion==2) ~

Some years ago when I used S-PLUS, I seem to remember, there was a discussion about a simple method of removing trailing spaces from vector elements. I'd be fairly sure the same would work in R, but I can't find any mention of anything like it in the R archives or with help.search(). There are ways I could do it with substring(), but I seem to remember there was something more elegant.

Hello, I am running R 2.6.1 on windows xp I am trying to fit a cox proportional hazard model with a shared Gaussian frailty term using coxme My model is specified as: nofit1<-coxme(Surv(Age,cen1new)~ Sex+bo2+bo3,random=~1|isl,data=mydat) With x1-x3 being dummy variables, and isl being the community level variable with 4 levels. Does anyone know if there is a way to get the standard error

Hi, I'm going crazy trying to plot a quite simple graph. i need to plot estimated hazard rate from a cox model. supposing the model i like this: coxPhMod=coxph(Surv(TIME, EV) ~ AGE+A+B+strata(C) data=data) with 4 level for C. how can i obtain a graph with 4 estimated (better smoothed) hazard curve (base-line hazard + 3 proportional) to highlight the effect of C. thanks!! laudan [[alternative

Hi, I am using rpart as a part of my masters' project. I am trying to print out the resulting model using plot() function along with text() function. I am having difficulties with labels being cut-off. In text() function, I am using use.n=T option to get the number of people in each nodes but the on the lower and left part of the plot, the numbers get cut off. Thanks! Linus [[alternative