similar to: alternative to logistic regression

Hello, I have interval censored data, censored between (0, 100). I used the tobit function in the AER package which in turn backs on survreg. Actually I'm struggling with the distribution. Data is asymmetrically distributed, so first choice would be a Weibull distribution. Unfortunately the Weibull doesn't allow for zero values in time data, as it requires x > 0. So I tried the

Hello, I have questions regarding penalized Cox regression using survival package (functions coxph() and ridge()). I am using R 2.8.0 on Ubuntu Linux and survival package version 2.35-4. Question 1. Consider the following example from help(ridge): > fit1 <- coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1), ovarian) As I understand, this builds a model in which `rx' is

Hello, I have some measurements that I am trying to fit a model to. I also have uncertainties for these measurements. Some of the measurements are not well detected, so I'd like to use a limit instead of the actual measurement. (I am always dealing with upper limits, i.e. left censored data.) I have successfully run survreg using the combination of well detected measurements and limits,

This issue has arisen within my anova.coxph routine, but is as easily illustrated with glm. testdata <- data.frame(y= 1:5, n= c(8,10,6,20,14), sex = c(0,1,0,1,1), age = c(30,20,35,25,40)) fit <- glm(cbind(y,n) ~ age + sex, binomial, data=testdata, model=TRUE) saveit <- fit$model update(fit, .~. - age, data=saveit)

Hi all, I found that the two different versions of "survival" packages, namely 2.36-5 vs. 2.36-8 or later, give different results for coxph function. Please see below and the data is attached. The second one was done on Linux, but Windows gave the same results. Could you please let me know which one I should trust? Thanks, ...Tao #####============================ R2.13.0,

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

Hi, if I got it right then the survival-time we expect for a subject is the integral over the specific survival-function of the subject from 0 to t_max. If I have a trained cox-model and want to make a prediction of the survival-time for a new subject I could use survfit(coxmodel, newdata=newSubject) to estimate a new survival-function which I have to integrate thereafter. Actually I thought

I stumbled onto this working on an update to coxph. The last 6 lines below are the question, the rest create a test data set. tmt585% R R version 2.12.2 (2011-02-25) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-unknown-linux-gnu (64-bit) # Lines of code from survival/tests/singtest.R > library(survival) Loading required package: splines

I was just curious if anyone knew of an alternative model to logistic regression where the probabilities seems pretty linear to the predictor rather than having that S shape that probit and logit assume. Maybe there is there some kind of other GLM that could accomplish that. Any textbook references or suggestions are appreciated. I have most of the texts but if someone knows of a text that talks

Hi all, I met with a problem about the weighted least square regression. 1. I simulated a Normal vector (sim1) with mean 425906 and standard deviation 40000. 2. I simulated a second Normal vector with conditional mean b1*sim1, where b1 is just a number I specified, and variance proportional to sim1. Precisely, the standard deviation is sqrt(sim1)*50. 3. Then I run a WLS regression without the

Dear all, I'm struggling with predicting "expected time until death" for a coxph and survreg model. I have two datasets. Dataset 1 includes a certain number of people for which I know a vector of covariates (age, gender, etc.) and their event times (i.e., I know whether they have died and when if death occurred prior to the end of the observation period). Dataset 2 includes another

Hello, I am trying to find an R function to compute 'direct adjusted survival' with standard errors. A SAS-macro to do this is presented in Zhang X, Loberiza FR, Klein JP, Zhang MJ. A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model. Comput Methods Programs Biomed 2007;88:95-101. It appears that this method is not implemented in R.

I would like to be able to construct hazard rates (or unconditional death prob) for many subjects from a given survfit. This will involve adjusting the ( n.event/n.risk) with (coxph object )$linear.predictors I must be having another silly day as I cannot reproduce the linear predictor: fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian) fit$linear.predictors[1] [1] 2.612756

I am trying to clear up all the "NOTE"s before a CRAN submission, but am a bit confused about this one.?? What is it complaining about -- that it doesn't like my name? ... * checking for file ?deming/DESCRIPTION? ... OK * this is package ?deming? version ?1.4-1? * checking CRAN incoming feasibility ... [7s/18s] NOTE Maintainer: ?Terry Therneau <therneau.terry at mayo.edu>?

Question: I am trying to impliment a function in R that we use quite regularly in Splus, and it fails due to a lack of the "terms.inner" function in R. The substitute is? Part question and part soapbox: Why remove terms.inner from R? It's little used, but rather innocuous. Mostly soapbox: I figured it was no big deal, as I originally discovered the use of terms.inner from

Hi, I would like write a split function to implement a new split method with the package RPART. I see that I can define my split function as specified in the example of usersplits function, but I don't understand how I can use the variable "offsets". What is the meaning of these variable? Thank's Michela

I'd like to get interaction terms in a model to be in another form. Namely, suppose I had variables age and group, the latter a factor with levels A, B, C, with age * group in the model. What I would like are the variables "age:group=A", "age:group=B" and "age:group=C" (and group itself of course). The coefficients of the model will then be the age effect

When I run R CMD check on the survival package I invariably get a note: ... * checking for file ?survival/DESCRIPTION? ... OK * this is package ?survival? version ?3.2-6? * checking CRAN incoming feasibility ... NOTE Maintainer: ?Terry M Therneau <therneau.terry at mayo.edu>? ... This is sufficient for the auto-check process to return the following failure message: Dear maintainer,

In my code below tempa and tempb are numeric vectors that I combined into a dataframe along with the deciles of tempa. I have an lapply statement that goes through the dataframe and does ten plots according to the appropriate decile. The code is below and it works fine. There are no bugs so I figure there was no need to include structure statements on the data. Also, I don't want to use coplot

Hi everyone, I'm a new R user (if this is a really basic question, please do excuse me...) and I'm having some questions regarding a deciles problem. I have a variable which I need to categorize according to its deciles (X). However, this categorization should be made into another variable (call it NewVar). Ex. for the quartiles case (just for the sake of exposition, since I need