search for: efron

Dear all, I want to apply weights to my sample data set and I am struggling with the Efron Approximation with weights. I have got one sample data shown as below: customer week arrest fin age race weight 1 weight 2 weight 3 1 20 1 1 27 1 2 15 2 2 17 1 0 18 1 2 19 1 3 25 1 1 19 0 2 20 1 4 52 0 1 23 1 2 5 1 5 52 0 0 19 0 2 11 1 6 25 1 0 19 0 2 26 1 I applied four different weight...

Hello: Could you possibly help me. In Efron's 2004 paper "Selection and Estimation ..." it was mentioned that so-called effect density estimate, denoted by g1(mu) was included in the locfdr package. However, I can't find it in the description of the package. Any suggestions? Sincerely, Nik -- View this message in context:...

...ogies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we did compare using the same approximations. I am wondering if this is a result of how coxph manages an ordered factor? Essentially, this is a survival dataset using tumor grade (1, 2, 3 and 4) as the risk factor. This is more of an...

...o make things even more confusing, Prentice introduced an "exact marginal likelihood" which is not implemented in R, but which SAS calls the "exact" method. Data is usually not truly discrete, however. More often ties are the result of imprecise measurement or grouping. The Efron approximation assumes that the data are actually continuous but we see ties because of this; it also introduces an approximation at one point in the calculation which greatly speeds up the computation; numerically the approximation is very good. In spite of the irrational love that our professi...

...14.84 2 1 2 0 1 1 08.63 A complication here is that units can experience repeated events while previous events are still ongoing. I tried the following: cox1 <- coxph( Surv( dur0, dur1, event) ~ strata(eventn) + x) This works fine under the breslow and efron method. However, since I have a fair number of ties, especially of repeated events while previous events are still ongoing, the exact method seems advisable. The help says that the exact method is computationally demanding, but even after days the computing it won't finish. Also, if I include...

Dear all, I am struggling with calculation of Schoenfeld residuals of my Cox Ph models. Based on the formula as attached, I calculated the Schoenfeld residuals for both non tied and tied data, respectively. And then I validated my results with R using the same data sets. However, I found that my results for non-tied data was ok but the results for tied data were different from R's. How

...tstrap analysis from a conditional logistic regression? The model has been built using the `clogit` function (`survival` package)? The model has the following structure: mod <- clogit(event ~ forest + log_area +forest:log_time + cluster(ID_individual) + strata(ID_strata), method = "efron", data = data , x=T, y=T) Using bootstrapping, I would like to have a measure of uncertainty around the estimates of beta coefficients. I am using the following code but I don't know how to consider strata and cluster arguments. library(boot) boot.clogit <- function(data, ind...

...Survival Function")  #construct a data frame for the plots plotdata <- data.frame(time = sumkap$time, surv = sumkap$surv, strata = sumkap$strata) fact.stage<-factor(larynx$stage) fit1<-coxph(Surv(time, death) ~ fact.stage + age.larynx + factor(year>=74), data=larynx, method=c("efron")) summary(fit1) avg.surv <- function(cfit, var.name, var.values, data, weights) { if(missing(data)){ if(!is.null(cfit$model)) mframe <- cfit$model elsemframe <-model.frame(cfit,sys.parent()) }   else mframe <- model.frame(cfit, data) var.num <- match(var.name, names(mframe)) da...

...a can be regarded as a complement and an extension to the 'survival' package. In fact eha requires survival. Eha contains three functions for proportional hazards analysis: 1. 'coxreg': Performs Cox regression, almost as 'coxph' in survival. There are two methods, 'efron' (default) and 'breslow', exactly as in coxph. There are two extensions, compared to coxph: (i) Sampling of survivors in risk sets (at event times), which can be useful with huge data sets and few events. (ii) The so-called 'weird bootstrap': For the fitted model, new events...

...a can be regarded as a complement and an extension to the 'survival' package. In fact eha requires survival. Eha contains three functions for proportional hazards analysis: 1. 'coxreg': Performs Cox regression, almost as 'coxph' in survival. There are two methods, 'efron' (default) and 'breslow', exactly as in coxph. There are two extensions, compared to coxph: (i) Sampling of survivors in risk sets (at event times), which can be useful with huge data sets and few events. (ii) The so-called 'weird bootstrap': For the fitted model, new events...

...problem for quadratic discriminant analysis). It thought this situation should be frequent enough to be mentioned in the literature, but I have found almost no mention in the references I have available, except for Hirst (see below). If I've reread correctly, this issue is not mentioned in Efron & Tibshirani (1997; the .632+ paper), or in Efron and Gong (the TAS "leisure look" paper), or the Efron & Tibshirani 1993 bootstrap book, or Chernick's "Bootstrap methods" book. I've only seen some side mentions in Ripley's Pattern recognition (when talkin...

...ere an R implementation of least angle regression for binary response modeling? I know that this question has been asked before, and I am also aware of the "lasso2" package, but that only implements an L1 penalty, i.e. the Lasso approach. Madigan and Ridgeway in their discussion of Efron et al (2004) describe a LARS-type algorithm for generalized linear models. Has anyone implemented this in R? Thanks for any help. Best, Ravi ---------------------------------------------------------------------------- ------- Ravi Varadhan, Ph.D. Assistant Professor, The Center o...

...ion `coxph.fit' called by `coxph' may fail to estimate the regression coefficients when some values of the design matrix are very large. For example library(survival) ### load example data load(url("http://www.imbe.med.uni-erlangen.de/~hothorn/coxph_fit.Rda")) method <- "efron" ### copied from `coxph.fit' coxfit <- .C("coxfit2", iter=as.integer(maxiter), as.integer(n), as.integer(nvar), stime, sstat, x= x[sorted,] , as.double(offset[sorted] - mean(offset)...

...ot;), > c("PTNO", "Treatment", "PFS_CENSORED", "PFS_MONTHS", "AGE", "PS2")) > cox3grp<- droplevels(cox3grp) > str(cox3grp) > > coxCV<- coxph(Surv(PFS_MONTHS, PFS_CENSORED == 1) ~ AGE + PS2, data=cox3grp, method = "efron") > coxCV > > drop1(coxCV, test="Chisq") > > require(car) > Anova(coxCV, type="III") > > And here are my results: > > cox3grp<- subset(survData, > + Treatment %in% c("DC", "DA", "DO"), >...

...urvival Function") #construct a data frame for the plots plotdata <- data.frame(time = sumkap$time, surv = sumkap$surv, strata = sumkap$strata) fact.stage<-factor(larynx$stage) fit1<-coxph(Surv(time, death) ~ fact.stage + age.larynx + factor(year>=74), data=larynx, method=c("efron")) summary(fit1) avg.surv <- function(cfit, var.name, var.values, data, weights) { if(missing(data)){ if(!is.null(cfit$model)) mframe <- cfit$model elsemframe <-model.frame(cfit,sys.parent()) } else mframe <- model.frame(cfit, data) var.num <- match(var.name, names(mframe)) da...

I am trying to plot survival curves using the following code as an example: >rs1799964.coxph<-(coxph(Surv(sassurvmonths,status)~age+stage+rs1799964_TNFA,method="efron")) >plot(rs1799964.coxph,lyt=c(1,3),xlab="Survival in Months",ylab="Proportion Surviving") I am gettingthe following error message: >Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ Any input to debugging this matter...

I have a model with quant vars only and the error message does not make sense: (mod1 <- coxph(Surv(time=strt,time2=stp,event=(resp==1))~ +incpost+I(amt/1e5)+rate+strata(termfac), subset=dt<"2010-08-30", data=inc,method="efron")) Call: coxph(formula = Surv(time = strt, time2 = stp, event = (resp == 1)) ~ +incpost + I(amt/1e+05) + rate + strata(termfac), data = inc, subset = dt < "2010-08-30", method = "efron") coef exp(coef) se(coef) z p incpost 0.256...

Hello, I downloaded R today because I was told it has very good bootstrapping abilities. What I need to do is to program it (or use an existing program) to bootstrapping test for multimodality using nonparametric kernel density estimates as proposed by Efron and Tibshirani (1993). If anyone can get me started, I will be immensely grateful. Thanks. Sangeeta -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help",...

...r) and confidence intervals for combinations of risk factors for my outcome. /* Combinations of Risk Factors */ data test2; input sex treat; DATALINES; 0 0 1 0 0 1 1 1 ; run; /* Survival estimates for the above combinations */ proc phreg data = pudat2; model withtime*wcens(0) = sex treat /ties = efron; baseline out = surv2 survival = survival lower = slower upper = supper covariates = test2 /method = ch nomean cltype=loglog; run; /* Survival estimates at 1 year */ proc print data = surv2 noobs; where withtime = 364; run; I now would like to do the same thing but in a competing risks set...

...?? resid <- .C(Ccoxscore, as.integer(n), ??? ??? ??? ??? as.integer(nvar), ??? ??? ??? ??? as.double(y), ??? ??? ??? ??? x=as.double(x), ??? ??? ??? ??? as.integer(newstrat), ??? ??? ??? ??? as.double(score), ??? ??? ??? ??? as.double(weights[ord]), ??? ??? ??? ??? as.integer(method=='efron'), ??? ??? ??? ??? resid= double(n*nvar), ??? ??? ??? ??? double(2*nvar))$resid Terry T. [[alternative HTML version deleted]]