similar to: Simple simulations

Dear all: Could anyone please provide some R codes to plot the below survival data to compare two groups (0 vs 1) after 2 simulations (TRL)? need 95% prediction interval on the plot from these 2 trials. I would like to simulate 1000 trials later. Thanks a lot for your great help and consideration! yan TRL ID ECOG BASE PTR8 GROUP POP ST ind 1 1 1 1 2.2636717 0.255634126 1 1 99.4 F 3 1 2 1

This code worked fine for me, then did some cleaning up of formatting using ESS (Emacs) and now I get this error, no idea what is causing it, all the brackets/parentheses seem to be balanced. What have I done wrong? Thanks Jim p0.trial01 <- 0.25 TruOR01 <- 0.80 num.patients.01 <- 50 num.trials.01 <- 5 LOR01.het.in <- 0.00 num.sims <- 1 simLOR01 <-

Dear R-help, I am trying to write a function to simulate datasets of size n which contain two time-to-event outcome variables with associated 'Event'/'Censored' indicator variables (flag1 and flag2 respectively). One of these indicator variables needs to be dependent on the other, so I am creating the first and trying to use this to create the second using an if/else statement.

I need help with these R simulations _______________________________________ The generation of pseudo-random numbers with uniform distribution [0.1] can be done through a method called Congruencial Committee, which consists the use of a recursive relationship of the type: xi +1 = (a.xi + c) mod m being a, c, and the positive integers m, c <m. Discuss in light of possible values of a, c, my

Hi Guy's I was wondering if someone could point me in the right direction. dbinom(10,1,0.25) I am using dbinom(10,1,0.25) to calculate the probabilty of 10 judges choosing a certain brand x times. I was wondering how I would go about simulating 1000 trials of each x value ? regards Brendan -- View this message in context:

Hi, I have some general questions about statistical analysis for a research dataset and a request for advice on using R and associated packages for a valid analysis of this data. I can only pose the problem as how to run multiple ANOVA tests on time series data, with reasonable controls of the family-wise error rate. If we run analysis at many small sections of a long time-series, the Type-I

Dear friends, If I have a table like this, first row A B C D ... are different levels of the variable, first column 0 1 2 4 ... are the levels of the "numbers", the numbers inside the table are the probabilities of the "number" occuring. A B C D ... 0 0.2 0.3 0.1 0.05 1 0.1 0.1 0.2 0.2 2 0.02 0.2 0 0.1 4 0.3 0.01 0.01

This concerns whether p-values from lmer can be trusted. From simulations, it seems that lmer can produce very small, and probably spurious, p-values. I realize that lmer is not yet a finished product. Is it likely that the problem will be fixed in a future release of the lme4 package? Using simulated data for a quite standard mixed-model anova (a balanced two-way design; see code for the

Dear R-help, I am writing some simulation code to create multiple sets of time-to-event clinical trial data (for use in meta-analysis). Within each trial, I want to apply censoring via simulation of uniform variables (with minimum zero and maximum the median outcome time for that particular trial). I have started by pre-allocating a matrix which has 3 columns; one for trial number, one for

Hi there, I am running Monte Carlo Simulations in R using ordinary "while (condition)" loops. Since the number of iterations is something like 100.000 and within each iteration a given subsample is extended sequentially it takes hours to run the simulation. Does anyone know if there is either a way to avoid using loops in Monte Carlo Simulations or how to include possible faster

I am trying to simulate a series of ones and zeros (1 or 0) and I am using "rbinom" but realizing that the number of successes expected is not accurate. Any advice out there. This is the example: N<-500 status<-rbinom(N, 1, prob = 0.15) count<-sum(status) 15 percent of 500 should be 75 but what I obtain from the "count" variable is 77 that gives the probability of

The Quantitative Decision Strategies group at Janssen Research & Development, Johnson & Johnson, is looking for a candidate to represent QDS in Beijing, China in the subsidiary company of Xian-Janssen Pharmacetical Ltd. The basic requirements for this candidate are 1) 3+ years experience in a quantitative field, but not necessarily pharmaceutical; 2) PhD in statistics or related field

Hi, I know this is not a mailing list for r, but I posted my question on several help pages and did not get any help. I really don't know how to solve my problem, maybe you could help me? want to simulate stock paths. I have simulated 1000 paths with 22 trading days (1 starting value). Now I want to include it into my presentation, but animated, so I need the png files. I want to create 1000

Hi to all the people (again), I'm doing some simulations with the memisc package of an own function, but I've the problem that I didn't know how to read the result of such simulations. My function is: > Torre<-function(a1,N1,a2,N2) + {Etorre<-(a1*N1)/(1+a1*N1) + Efuera<-(a2*N2)/(1+a2*N2) + if(Etorre>Efuera)Subir=TRUE + if (Etorre<Efuera)Subir=FALSE +

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

Hi all, I am a biostatistician and I have developed my own ranking system for clinical data. I would like to test the efficiency of it w.r.t. to other ranking systems. I would like to simulate the data and after assigning ranks to my observed scores(after neglecting dropouts), observe the type I error. If I want to do a Kruuskal Wallis type of test, what test statistic should I use to test for a

I tried the code that Richard O'Keefe posted last week, to wit: library(chron) ymd.to.POSIXlt <- function (y, m, d) as.POSIXlt(chron(julian(y=y, x=m, d=d))) n <- 100000 y <- sample(1970:2004, n, replace=TRUE) m <- sample(1:12, n, replace=TRUE) d <- sample(1:28, n, replace=TRUE) system.time(ymd.to.POSIXlt(y, m, d)) [1] 8.78 0.10

Dear R-users, I am trying to perform some simulations from a model defined by ordinary differential equations. I would be grateful if someone could indicate some functions/packages/examples I could look at. Thank you in advance. Sebastien

Hello, I have estimated an ARIMA model and I would like to make simulations from this estimated model 1,5 and 10 steps ahead. Does anybody know how to do this? Thank You Felipe Parra [[alternative HTML version deleted]]

Hi. I'm embarking on my first attempt at creating my own panel function for lattice graphics, and despite all of my online research and pouring through the documentation, I cannot figure out how to solve my particular problem. Hopefully, a generous fellow R user can help. I have some data that is split into two groups: some "actual" data, and some simulated data,