similar to: how do I write Rd file for this?

I'm having difficulties with plot.locfit.3d, at least I think that is the problem. I have a large dataframe (about 4 MM cases) and was hoping to see a non-parametric estimate of the hazard plotted against two variables: > fit <- locfit(~surv.yr+ ur_protein + ur_creatinine, data=TRdta, cens = 1-death, family = "hazard", xlim=c(0,10)) # it took somewhere between 1 and 2

I installed the locfit package under Linux (gcc 2.7.2). Installation was ok but > x <- runif(200) > y.compl <- 10*x*x*rgamma(200,3) > med.y <- median(y.compl) > cens <- ifelse(y.compl<=med.y,1,0) > y <- cens * y.compl + (1-cens)*med.y > library(locfit) > m <- locfit(y~x,cens=cens,family="gamma") /usr/local/src/R-0.61.1/bin/R.binary: can't

Dear R-help How can one get survplot() to place the number at risk just below the survival curve as opposed to the default which is just above the x-axis? I tried the code bellow but the result is not satisfactory as some numbers are repeated several times at different y coordinates and the position of the n.risk numbers corresponds to the x-axis tick marks not the survival curve time of

I have a problem with the use of locfit with censured data, when I carry out locfit by: fitbmt<-locfit(~recur,data=BMTAGE11,cens=df.status,family="hazard",alpha=0.5) it does not give me any message, but if I want to obtain the graph or even if I ask for (fitbmt) made it gives me the following message: > fitbmt31 Problem: Object "fitbmt31" not found, while calling

Dear all, I am trying to plot KM curves with confidence bands with function survplot under package rms. However, the following codes do not seem to work. The KM curves are produced, but the confidence bands are not there. Any insights? Thanks in advance. library(rms) ########data generation############ n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age"

Hi, I'm trying to work through the examples and code in Loader's LOCAL REGRESSION AND LIKELIHOOD, and have run into a problem with the code for one sided smoothing and change point analysis (p. 110-112). The code, after loading locfit: midp<-(1945:1988)+0.5 fitl<-locfit(thickness~left(year), data=penny, alpha=c(0,10), deg=1, ev=midp)

Hello, I wish to?censor 10% of my sample units of 50 from a Weibull distribution. Below is the code for it. I will need to know whether what i have done is correct and if not, can i have any suggestion to improve it? Thank you ?p=2;b=120 n=50 r=45 t<-rweibull(r,shape=p,scale=b) meantrue<-gamma(1+(1/p))*b meantrue cen<- runif(n-r,min=0,max=meantrue) cen Chris Guure Researcher,

Hi , I am trying to get some estimator based on lognormal distribution when we have left,interval, and right censored data. Since, there is now avalible pakage in R can help me in this, I had to write my own code using Newton Raphson method which requires first and second derivative of log likelihood but my problem after runing the code is the estimators were too high. with this email ,I provide

Hello everyone, I'm trying to update the locfit package so that it passes package check in R 2.6.0. However, the check seems to think some of the functions with dot in the names are S3 methods (thus warns about the format of the \usage{} part) when they are not. Can anyone recommend a workaround for this? I tried reading R-exts, but couldn't find any hint. I'd very much appreciate

I am trying to reproduce Figure 10.5 of Loader's book: Local Regression and Likelihood. The code provided in the book does not seem to work. I have managed (a while ago) to get the accompanied R-code for the figures in the book (file called lffigs.R) from somewhere - cannot find it on the web anymore. The code in the .R script file does not work either. Could anybody please direct me in

Dear, I wanna to compare AUC generated by two distribution models using the same sample. I tried improveProb function's example code below. set.seed(1) library(survival) x1 <- rnorm(400) x2 <- x1 + rnorm(400) d.time <- rexp(400) + (x1 - min(x1)) cens <- runif(400,.5,2) death <- d.time <= cens d.time <- pmin(d.time, cens) rcorrp.cens(x1, x2, Surv(d.time, death))

Hi R-helpers, I am having a problem understanding how to construct the qmatrix, i have read the help menu for msm but I am still a bit lost. Below is an example of one of my transition matrices: statetable.msm(BEH, ID, data = A1) to from 1 2 3 4 5 23 200 201 203 999 1 86 11 2 20 1 9 3 11 1 22 2 18 4 4 4 0 1 1 1 0 1 3 5 2 0 5 0 0 2 0 0 3

Hello, ?can i implement this as 10% censored data where t gives me failure and x censored. Thank you p=2;b=120 n=50 set.seed(132); r<-sample(1:50,45) t<-rweibull(r,shape=p,scale=b) t set.seed(123);? cens <- sample(1:50, 5)? x<-runif(cens,shape=p,scale=b)? x Chris Guure Researcher, Institute for Mathematical Research UPM

Hi, I was very much hoping someone could help me with the following. I am trying to convert some SAS NLMIXED code to NLME in R (v.2.1), but I get an error message. Does anyone have any suggestions? I think my error is with the random effect "u" which seems to be parametrized differently in the SAS code. In case it's helpful, what I am essentially trying to do is estimate parameters

Hi, I am trying to simulate a regression on survival data under a few conditions: 1. Under different error distributions 2. Have the error term be dependent on the covariates But I'm not sure how to specify either conditions. I am using the Design package to perform the survival analysis using the survreg, bj, coxph functions. Any help is greatly appreciated. This is what I have so far:

Dear mailing list, I tried to run the example for the conditional bootstap written in the help file of censboot. I got the following result: STRATIFIED CONDITIONAL BOOTSTRAP FOR CENSORED DATA Call: censboot(data = aml, statistic = aml.fun, R = 499, F.surv = aml.s1, G.surv = aml.s2, strata = aml$group, sim = "cond") Bootstrap Statistics : original bias std. error t1*

Hi, I?m trying to combine the Locfit Package with the Mgcv package (to use Generalized Additive Models with Locfit components). I read the book written by Clive Loader where it?s said that, for the S language, you just have to "load" the locfit package using the command : Library(locfit, first="T") in order to use locfit components in an additive model. But I can?t. I guess

Hi list, This was asked a couple of years ago but I can't find a resolution. Is there any way to get the coefficients from one of the local polynomial fits in locfit. I realize that locfit only constructs polynomials at a handful of intelligently selected points and uses interpolation to predict any other points. I would like to know the terms of the polynomials at these points. It seems

Dear all, I am interested to evaluate reclassification using net reclassification improvement and Integrated Discrimination Index IDI after survival analysis (Cox proportional hazards using stcox). I search a R package or a R code that specifically addresses the categorical NRI for time-to-event data in the presence of censored observation and, if possible, at different follow-up time points. I

Hello I've decided to try and distill an earlier rather ill focused question to try and elicit a response. Any help is greatly appreciated. Why does mod.cox not work with sbrier whilst mod.km does? Can I make it work? > data(DLBCL) > DLBCL.surv<-Surv(DLBCL$time,DLBCL$cens) > > mod.km<-survfit(DLBCL.surv) > mod.cox<-survfit(coxph(DLBCL.surv~IPI, data=DLBCL)) >