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]]
>
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>
--
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
