similar to: bootstrap in time dependent Cox model‏

Dear List, After including cluster() option the coxreg (from eha package) produces results slightly different than that of coxph (from survival) in the following time-dependent treatment effect calculation (example is used just to make the point). Will appreciate any explaination / comment. cheers, Ehsan ############################ require(survival) require(eha) data(heart) # create weights

Dear List, I have a theoretical question related to epidemiological data analysis: If the treatment status (tx = 0,1) changes over time for the patients in a non-randomized cohort, is there a way to estimate the treatment effect? (i.e., after joining the study, some patients may have to wait for a period of time before receiving the treatment, i.e., the situation of patient with id == 2 for the

Dear List, Say, we generate data like this- dat<-rnorm(1000,1,2) hist(dat) How do i make the histogram, say, red (col = 2) before X = dat = 0, and rest say, green (col = 3) beyond X = dat = 0 in R? The resulting histogram could be like this http://ehsan.karim.googlepages.com/histogram.JPG (edited) Thanks in advance. Ehsan http://ehsan.karim.googlepages.com/diaryofastatistician

Dear List Members, I need some help about programming in S language. My problem is as follows: I have meteorological data (about rainfall measurement each day from 1989-2002), say like http://www.angelfire.com/ab5/get5/data.rainfall.txt or http://www.angelfire.com/ab5/get5/R.rainfall.txt in a sequence of 0(denoting dry day)'s and 1(denoting wet day)'s. I want to construct a frequency

Dear List, Can anyone please explain the difference between cluster() and frailty() in a coxph? I am a bit puzzled about it. Would appreciate any useful reference or direction. cheers, Ehsan > marginal.model <- coxph(Surv(time, status) ~ rx + cluster(litter), rats) > frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats) > marginal.model Call: coxph(formula =

Dear all, I was wondering if there is any way i could do a "Grid Search" on a parameter space using R (as SAS 6.12 and higher can do it) to start the Newton-Gauss Linearization least squares method when i have NO prior information about the parameter. W. N. Venables and B. D. Ripley (2002) "Modern Applied Statistics with S", 4 th ed., page 216-7 has a topic

Dear all, Does R or S-plus or any of their packages provide any command to form any of the following procedures to find Best Regression Equation - 1. 'All Possible Regressions Procedures' (is there any automated command to perform 2^p regressions and ordering according to criteria R2(adj), mallows Cp, s2- by not setting all the regression models manually), 2. 'Backward

Dear List, Suppose, I have some observed and expected frequencies, such as following. I need to draw a graph where plots of observed and expected frequencies are merged into one. ------------------------------------------------ m <- c(1,2,3,4,5,6,7,8,9,10,12,13,17) k <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 19) ExpWW <- c(0.309330628803245, 0.213645190887434, 0.147558189649435,

Dear List, I am not sure how should i optimize a log-likelihood numerically: Here is a Text book example from Statistical Inference by George Casella, 2nd Edition Casella and Berger, Roger L. Berger (2002, pp. 355, ex. 7.4 # 7.2.b): data = x = c(20.0, 23.9, 20.9, 23.8, 25.0, 24.0, 21.7, 23.8, 22.8, 23.1, 23.1, 23.5, 23.0, 23.0) n <- length(x) # likelihood from a 2 parameter Gamma(alpha,

Dear all, Can any one tell me how can i perform Orthogonal Polynomial Regression parameter estimation in R? -------------------------------------------- Here is an "Orthogonal Polynomial" Regression problem collected from Draper, Smith(1981), page 269. Note that only value of alpha0 (intercept term) and signs of each estimate match with the result obtained from coef(orth.fit). What

Dear all, Does R or S-plus or any of their packages provide Non-parametric "Run test" (which tests whether a sequence of numbers might be random or not)? If yes, i'd like a numerical illustration of this test. Any response / help / comment / suggestion will be greatly appreciated. Thanks in advance. ------------------------------- Mohammad Ehsanul Karim <wildscop at

Dear all, Can anyone please help me about any of the following questions: -------------------------------------------- 1. How can i find "factorial" of any number in R? I tried > prod(170:1) # to find factorial of 170 or 170! Is it the only procedure - or R has any better process / operational character to calculate factorial? Also, is it possible to calculate factorial of 500? Or

Dear all, I would like to know if it is possible to fit in R a Cox ph model with time-dependent covariates and to account for hierarchical effects at the same time. Additionally, I'd like also to know if it would be possible to perform any feature selection on this model fit. I have a data set that is composed by multiple marker measurements (and hundreds of covariates) at different time

Dear List, If any of observed and/or expected data has less than 5 frequencies, then chisq.test (Pearson's Chi-squared Test for Count Data from package:stats) gives warning messages. For example, x<-c(10, 14, 10, 11, 11, 7, 8, 4, 1, 4, 4, 2, 1, 1, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) y<-c(9.13112391745095, 13.1626482033341, 12.6623267638188, 11.0130706413029, 9.16415925139016,

Dear List, How do I extract the approximate Wald test for the frailty (in the following example 17.89 value)? What about the P-values, other Chisq, DF, se(coef) and se2? How can they be extracted? ######################################################> kfitm1 Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id, dist = "gauss"), data = kidney)

Dear All, I am contemplating centering the covariates in my Cox model to reduce multicollinearity between the predictors and the interaction term and to render a more meaningful interpretation of the regression coefficient. Suppose I have two indicator variables, x1 and x2 which represent age categories (x1 is patients less than 16 while x2 is for patients older than 65). If I use the following

Dear all, I am thinking of writing my own functions in s-plus (or in R). I just know how to work with S-plus / R built-in functions. Therefore, I'm a beginner in S programming. I am looking for some on-line documentation that is well written about "Programming in S language" where control stuctures / loops / vectorization and necessery sequences of S programming are

Dear R-list users, I am trying to do simulation of survival data to enable it to run under frailty option. Below is the function a that I am using. My questions are: 1. How do I modify it to get bigger (hopefully significant) value of Variance of random effect? 2. What changes do I have to make in the function to run it under correlated frailty model? (may be in kinship package) 3. Is there

Dear all, For estimating Cobb-Douglad production Function [ Y = ALPHA * (L^(BETA1)) * (K^(BETA2)) ], i want to use nls function (without linearizing it). But how can i get initial values? ------------------------------------ > options(prompt=" R> " ) R> Y <- c(59.6, 63.9, 73.5, 75.6, 77.3, 82.8, 83.6, 84.9, 90.3, 80.5, 73.5, 60.3, 58.2, 64.4, 75.4, 85, 92.7, 85.4,

Hi, I'm reading van Belle et al "Biostatistics" and trying to run a cox test using a dataset from: http://faculty.washington.edu/~heagerty/Books/Biostatistics/chapter16.html (Primary Biliary Cirrhosis data link at top of the page), I'm using the following code: --------------- start of code library(survival) liver <-