search for: martingaleness

Hi list, I was trying to use "predict.coxph" to calculate martingale residuals on a test data, however, as pointed out before http://tolstoy.newcastle.edu.au/R/e4/help/08/06/13508.html predict(mycox1, newdata, type="expected") is not implemented yet. Dieter suggested to use 'cph' and 'predict.Design', but from my reading so far, I'm not sure they can

Full_Name: Rob Strawderman Version: 1.9.1 OS: red hat linux 8 Submission from: (NULL) (128.253.217.211) The following demonstrates the basic problem: [martingale <99>]% rm .RData rm: remove regular file `.RData'? y [martingale <100>]% R R : Copyright 2004, The R Foundation for Statistical Computing Version 1.9.1 (2004-06-21), ISBN 3-900051-00-3 [deleted] >

Is there an implementation of the Cox-Snell residuals / Nelson-Aalen plot for goodness of fit? Or otherwise is there an appropriate Goodness of Fit diagnostic? Thanks Murray -- Murray Pung Statistician, Datapharm Australia Pty Ltd 0404 273 283 [[alternative HTML version deleted]]

Hi Gurus: In the coxph() objects in Survival package, there is an attribute called residuals. Usually, there are several kinds for censored survival data. I can't seem to find in the documentation as to which one this is calculating. Anyone knows? Karen _________________________________________________________________ [[alternative HTML version deleted]]

Hi there, I am currently working on my dissertation which is about testing the martingale hypothesis in the stock market using a methodology involving a range of variance ratio tests and multiple variance ratio tests. I contacted the author of a reference paper and I was told that the tests can be conducted using R programming language. Although I have gone through the theoretical background of

Hi Folks, Hopefully this question has enough R and not too much stats to be appropriate for this list. Based on,* Hosmer et al. 2013. Logistic regression for matched case-control studies. Applied Logistic Regression *(eqtn. 7.8)*, *I am assessing GOF of conditional (or matched) logistic regression models with the *standardized Pearson residuals*. The authors define ?large? as delta chi-squared

Hey fellas: In the context of the gambler's ruin problem, the following R code obtains the mean duration of the game, in turns: # total.capital is a constant, an arbitrary positive integer # initial.capital is a constant, an arbitrary positive integer between, and not including # 0 and total.capital # p is the probability of winning 1$ on each turn # 1-p is the probability of loosing 1$ # N

Hello everyone, I am searching for a covariate selection procedure in a cox model formulated as a counting process. I use intervals, my formula looks like coxph(Surv(start,stop,status)~ x1+x2+...+cluster(id),robust=T) where id is a country code (I study occurence of civil wars from 1962 to 1997). I'd like something not based on p-values, since they have several flaws for this purpose. I turned

Hi all, I've uploaded STAR (Spike Train Analysis with R) on CRAN two days ago. The package is designed to analyze neuronal spike (action potential) trains. It uses S3 classes and methods and makes heavy use of other CRAN packages like gss, R2HTML, mgcv, survival. * Analysis of both spontaneous and stimulus evoked activity is implemented for single neuron spike trains as well as for many

Hello. I am making a program that allows a user to enter a formula to be used in coxph() and then does some other stuff to it, and also allows the user to set other options within coxph(). What is the proper way to pass a formula to a function along with a ...? I have a function: func <- function(coxformula, data = parent.frame(),...) { # Cut to relevant part of function coxmod <-

Hi, is there any method for goodness of fit testing of an (as general as possible) univariate distribution with parameters estimated, for normal, exponential, gamma distributions, say (e.g. the corrected p-values for the Kolmogorov-Smirnov or Chi-squared with corresponding ML estimation method)? It seems that neither ks.test nor chisq.test handle estimated parameters. I am aware of function

Hi all, I know pnorm() is able to calculate a sequence of probabilities by providing a vector of means, and a single number for location and standard deviation. For example, pnorm(0.5,c(0.5,2),1) will produce [1] 0.5000000 0.0668072. However, I wonder if its multivariate counterpart mvtnorm can do the same thing? Namely, if I provide a sequence of (vector-)means and a single vector for

Hi all, I've uploaded STAR (Spike Train Analysis with R) on CRAN two days ago. The package is designed to analyze neuronal spike (action potential) trains. It uses S3 classes and methods and makes heavy use of other CRAN packages like gss, R2HTML, mgcv, survival. * Analysis of both spontaneous and stimulus evoked activity is implemented for single neuron spike trains as well as for many

Hi I want to compare the survival curves in two groups. Because the hazards are not proportional (the curves cross) the log rank test or Cox proportional hazard test are not suitable. How should such curves be compared? Comands are welcome.... Thanks in advance

Hello, My question concerns model selection, stepAIC(), add1(), and coxph(). In Venables and Ripley (3rd Ed) pp389-390 there is an example of using stepAIC() for the automated selection of a coxph model for VA lung cancer data. A statistics question: Can partial likelihoods be interpreted in the same manner as likelihoods with respect to information based criterion and likelihood ratio tests?

Hi Louis, It's simple to run a r script from the excel spreadsheet. Just write your code, source("C:\\Quantil Aplicativos\\Genercauca\\BackwardSelectionNC.r"), into a cell of a workingsheet. Then right-click the cell and select "run code" in the pop-up menu. Hope this will help you. Best, Bernard -----????----- ???: r-help-bounces at r-project.org [mailto:r-help-bounces