similar to: Replacing sets of rows in matrix within a loop

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.

Hello ALL: I ran with success the following commands in R getting a file saved ------------------------------------------------------------------------------------ postscript() postscript('~/data/st202/2003/lecture00/lecture00-graph-01.eps', horizontal = FALSE, height = 6, pointsize = 10) hist(trial.outcome.5, breaks = 5, main = '1000 Replications of 5 Trials of a

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 <-

Hi, I have been looking for an R package which allowed to do meta-analysis (both pairwise and network/mixed-treatment) at arm-level rather than at trial-level, the latter being the common way in which meta-analysis is done. By arm-level meta-analysis I mean one that accounts for data provided at the level of the individual arms of each trial and that does not simply derive the difference between

Dear R - Users I have some problems fitting a linear mixed effects model using the lme function (nlme library). A sample data is as shown at the bottom of this mail. I fit my linear mixed model using the following R code: bmr <-lme (outcome~ -1 + as.factor(endpoint)+ as.factor(endpoint):trt, data=datt, random=~-1 + as.factor(endpoint) + as.factor(endpoint):trt|as.factor(Trial),

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

Hello, I am confused about the order of nesting in mixed models using functions like aov(), lme(), lmer(). I have the following data: n subjects in either condition A or B each subject tested at each of 3 numerical values ("distance" = 40,50,60), repeated 4 times for each of the 3 numerical values ("trial" = 1,2,3,4) Variable summary: Condition: 2 level factor Distance:

Dear R-gurus: I have a question about lmer. Basically, I have a dataset, in which each observation records number of trials (N) and number of events (Y) given a covariate combination(X) and group id (grp_id). So, my dataset is in tabular form. (in case my explanation of tabular form is unclear, please see the link:

Full_Name: Uffe H?gsbro Thygesen Version: 2.2.0 OS: linux Submission from: (NULL) (130.226.135.250) Hello all. pbinom(q=0,size=0,prob=0.5) returns the value NaN. I had expected the result 1. In fact any value for q seems to give an NaN. Note that dbinom(x=0,size=0,prob=0.5) returns the value 1. Cheers, Uffe

Dear List, I am (again) looking at meta-regression as a way to refine meta-analytic results. What I want to do is to assess the impact of some fixed factors on the results of a meta-analysis. Some of them may be crossed with the main factor of the meta-analysis (e. g. clinical presentation of a disease, defining subgroups in each of the studies under analysis), some of them may be a grouping

Dear R users, I have a large individual-level dataset (~700,000 records) which I am performing a conditional logistic regression on. Key variables include the dichotomous outcome, dichotomous exposure, and the stratum to which each person belongs. Using this individual-level dataset I can successfully use clogit to create the model I want. However reading this large .csv file into R and running

Hi Folks: **Off Topic** Those interested in clinical trials may find the following of interest: http://www.nytimes.com/2010/09/19/health/research/19trial.html It concerns the ethicality of randomizing those with life-threatening disease to relatively ineffective SOC when new "biologically targeted" therapies "appear" to be more effective. While the context may be new, the

I cannot seem to get a L'abbe plot to work on R. I do not understand what the X coordinates, or alternatively an object of class metabin, is supposed to mean. What is a class of metabin? Institute of Behavioral Genetics University of Colorado, Boulder Whitney.Melroy at Colorado.EDU

Dear List, yes, me again trying to work with qplot ;-) I would like to make several single plots within a loop, like this (simplified and so on...): trials <- c("A","B","C") mycolours <- ("wheat","darkolivegreen","lightgreen",

Dear everybody! My fixed-effects-only model looks like this: glmer(Accuracy ~ C.RT*Group, data = da) C.RT is the reaction time variable, and Group is a categorical variable with 0 and 1 as values. I would like to specify that main intercept, Group intercept, C.RT slope and C.RT*Group slope vary across subjects and trials. All subjects have values in Group = 0 and in Group = 1. Trials are nested

Dear all, I have got two vectors coding for a stimulus presented in the current trial (mydat$Stimulus) and a prediction in the same trial (mydat$Prediciton), respectively. By applying an if-conditional I want to create a new vector that indicates if there is a match between both vectors in the same trial. That is, if the prediction equals the stimulus. When I pick out some trials randomly, I get

I am looking for help to analyze an unbalanced AB/BA cross-over design by requesting the type III SS ! # Example 3.1 from S. Senn (1993). Cross-over Trials in Clinical Research outcome<-c(310,310,370,410,250,380,330,270,260,300,390,210,350,365,370,310,380,290,260,90,385,400,410,320,340,220) subject<-as.factor(c(1,4,6,7,10,11,14,1,4,6,7,10,11,14,2,3,5,9,12,13,2,3,5,9,12,13))

Hello everybody! I use R 1.6.2 in Windows, and have a problem controlling the na.action. In a dataset with twelve trials, one of the trials lack any readings of the variable "STS.SH" (standing power at harvest) Fitting an aov() object with the call: led1t7sts.aov <- aov(STS.SH ~ Trial/Block + Treatment + Treatment:Trial, data = led1t7, na.action=na.exclude) seems to work as it

Dear list members, I have a series of /unequal/ probabilities [p1,p2,...,pk], describing mutually exclusive events, and a "remainder" class with a probability p0=1-p1-p2-....-pk, and need to calculate, for a given number of trials t>=k, the combined probability that each of the classes 1...k contains at least 1 "event" (the remainder class may be empty). To me this reaks

Hi there, I need some help to figure out what is the proper model in survival analysis for my data. Subjects were randomized to 3 treatments in trial 1, some of them experience the event during the trial; After period of time those subjects were randomized to 3 treatments again in trial 2, but different from what they got in 1st trial, some of them experience the event during the 2nd trial (I