Hi Dimitris, Appreciate for your reply with detailed information, many thanks!
I realize that generating random number won't be so simple more than I expected, but got some hints from the advice. I am actually hoping to do a parametric bootstrap likelihood test, because this is the way of testing for glmm result what I understood. Following is what I would like to do: # settings n <- number of samples y <- c(2 2 1 0 0 2 0 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 0 2 0 2 2) x <- c(22 22 24 21 26 18 23 21 17 15 22 24 21 17 26 15 16 13 22 15 15 23 16 23 18 37 22 30) #dependent=y #independent=x Then, I would like to generate random number on glmm = random.y <- rbinom (num,n,p) to do this test. However, this only hypothesized binomial distribution, not include normal one. In this case, how would you be able to do? Apologize if I didn't understand correctly what you wrote and make confuse you. Greatly appreciated if you help me. Any advice would be wonderful! Best reagards, Odette On Thu, Nov 20, 2008 at 8:24 PM, Dimitris Rizopoulos < [EMAIL PROTECTED]> wrote: > check the following code: > > # settings > n <- 100 # number of sample units > p <- 10 # number of repeated measurements > N <- n * p # total number of measurements > t.max <- 3 > > # parameter values > betas <- c(0.5, 0.4, -0.5, -0.8) # fixed effects (check also 'X' below) > sigma.b <- 2 # random effects variance > > # id, treatment & time > id <- rep(1:n, each = p) > treat <- rep(0:1, each = n/2) > time <- seq(0, t.max, length.out = p) > > # simulate random effects > b <- rnorm(n, sd = sigma.b) > > # simulate longitudinal process conditionally on random effects > time.rep <- rep(time, n) > treat.rep <- rep(treat, each = p) > X <- cbind(1, treat.rep, > time.rep, treat.rep * time.rep) # fixed effects design matrix > muY <- plogis(c(X %*% betas) + b[id]) # conditional probabilities > y <- rbinom(N, 1, muY) # simulate binary responses > > # put the simulated data in a data.frame > simulData <- data.frame( > id = id, > y = y, > treat = treat.rep, > time = time.rep > ) > > # fit the model > library(glmmML) > fit <- glmmML(y ~ treat * time, data = simulData, cluster = id) > summary(fit) > > > I hope it helps. > > Best, > Dimitris > > > Odette Gaston wrote: > >> Hi everybody, >> >> I am currently working on glmmML() and wish to generate random number to >> do >> some tests, however, glmm was hypothesized the mixed distributions with >> normal and binomial in terms of having a random effect. How would you be >> able to generate random number in this case? Is there a function in R to >> generate random number of mixed distribution (normal+binomial)? Any >> comments would be appreciated. >> >> Many thanks, >> Odette >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html> >> and provide commented, minimal, self-contained, reproducible code. >> >> > -- > Dimitris Rizopoulos > Assistant Professor > Department of Biostatistics > Erasmus Medical Center > > Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands > Tel: +31/(0)10/7043478 > Fax: +31/(0)10/7043014 > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
