similar to: Efron's locfdr package - a component missing

For time scale that are truly discrete Cox proposed the "exact partial likelihood". I call that the "exact" method and SAS calls it the "discrete" method. What we compute is precisely the same, however they use a clever algorithm which is faster. To make things even more confusing, Prentice introduced an "exact marginal likelihood" which is not

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 weighted Efron

Dear R-help members, Apologies - 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

I'm trying to estimate a cox proportional hazards regression for repeated events (in gap time) with time varying covariates. The dataset consists of just around 6000 observations (lines) (110 events). The (stylized) data look as follows: unit dur0 dur1 eventn event ongoing x 1 0 1 0 0 0 32.23 1 1 2 0 1 1 35.34 1

Nelly Reduan a partag? un fichier OneDrive avec vous. Pour l?afficher, cliquez sur le lien ci-dessous. <https://1drv.ms/u/s!Apkg2VlgfYyDgRAeVIM0nEajx0Fb> [https://r1.res.office365.com/owa/prem/images/dc-png_20.png]<https://1drv.ms/u/s!Apkg2VlgfYyDgRAeVIM0nEajx0Fb> Screenshot 2017-11-12 18.49.43.png<https://1drv.ms/u/s!Apkg2VlgfYyDgRAeVIM0nEajx0Fb> Hello How can I perform

Hi, Is there 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

I should hope that there is trouble, since "type III" is an undefined concept for a Cox model. Since SAS Inc fostered the cult of type III they have recently added it as an option for phreg, but I am not able to find any hints in the phreg documentation of what exactly they are doing when you invoke it. If you can unearth this information, then I will be happy to tell you whether

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),

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) :

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

Dear all, I was wondering if anyone can help? I am an R user but recently I have resorted to SAS to calculate the probability of the event (and the associated confidence interval) for the Cox model with combinations of risk factors. For example, suppose I have a Cox model with two binary variables, one for gender and one for treatment, I wish to calculate the probability of survival for the

I am now getting the occasional complaint about survival routines that are not able to handle big data.?? I looked in the manuals to try and update my understanding of max vector size, max matrix, max data set, etc; but it is either not there or I missed it (the latter more likely).?? Is it still .Machine$integer.max for everything??? Will that change??? Found where? I am going to need to go

Hello All, Despite my preference for reporting confidence intervals, I need to obtain a p-value for a hypothesis test in the context of regression using bootstrapping. I have read John Fox's chapter on bootstrapping regression models and have consulted Efron & Tibshirani's "An Introduction to the Bootstrap" but I just wanted to ask the experts here for some feedback to make

Hi, I presently working with the bootstrap methodology. I am trying to establish confidence intervals using the parametric ABC method with the bivariate normal data. I looked at the paper written by DiCiccio and Efron and did not find any programming materials which was used in that paper. So I am in confuse how to aply bootstrap ABC parametric confidence intervals method. I will be grateful to

The following code works in Splus but not in R coxph <- function(formula, data, weights, subset, na.action, init, control, method= c("efron", "breslow", "exact"), singular.ok =TRUE, robust=FALSE, model=FALSE, x=FALSE, y=TRUE, ...) { method <- match.arg(method) Call <- match.call() # create a call to model.frame() that contains the formula

Dear Sirs, I am a R beginning user: by mean of R I would like to apply the bootstrap to my data in order to test cost differences between independent or paired samples of people affected by a certain disease. My problem is that even if I am reading the book by Efron (introduction to the bootstrap), looking at the examples in internet and available in R, learning a lot of theoretical things on

I am using Efron's local fdr procedure. But, I want to change the null from N(0,1) to N(0, 0.002). I can access the function but I have no idea what to change. In other words, I want nulltype to be N(0,0.002) instead of N(0,1) in his function. Anyone has any ideas. This is his code for the local fdr: function (zz, bre = 120, df = 7, pct = 0, pct0 = 1/4, nulltype = 1, type = 0, plot = 1,

I have a dataset with event=death time (from medical examination until death/censoring) dose (given at examination time) Two groups are considered, a non-exposed group (dose=0), an exposed group (dose between 5 and 60). For some reason there is a theory of the dose increasing its effect over time (however it was only given (and measured) once = at the time of examination). I tested a model:

The following fit does not make sense to me, please, correct me if I have a logical error. > moddowsn Call: coxph(formula = Surv(start, stop, resp) ~ sn + matfac2, data = coxsn1, method = "efron") coef exp(coef) se(coef) z p sn2 0.0497 1.051 0.02030 2.450 1.4e-02 sn3 -0.0532 0.948 0.02038 -2.610 9.0e-03 sn4 -0.0410 0.960

This is a simple R program that I have been trying to run. I keep running into the "singular matrix" error. I end up with no sensible results. Can anyone suggest any changes or a way around this? I am a total rookie when working with R. Thanks, Rasika > library(survival) Loading required package: splines > args(coxph) function (formula, data, weights, subset, na.action, init,