Dear R-help community,
I would like to simulate type I error for a random-effects model I
generated.
The statistic of interest is standard deviations of the random intercept
and random slope. Specifically, for random intercept, H_{0}: lambda_{0} =2
and H_{1}: lambda_{0} not equal to 2; for random slope, H_{0}: lambda_{1}
=1 and H_{1}: lambda_{1} not equal to 1. I assume the test would be
likelihood ratio test but please correct me if I am wrong. How do I assess
type I error for the random-effects model I specified below:
set.seed(323)
#The following code is to specify the structure and parameters of the
random-effects model
dtfunc = function(nsub){
time = 0:9
rt = c()
time.all = rep(time, nsub)
subid.all = as.factor(rep(1:nsub, each = length(time)))
# Step 1: Specify the lambdas.
G = matrix(c(2^2, 0, 0, 1^2), nrow = 2)
int.mean = 251
slope.mean = 10
sub.ints.slopes = mvrnorm(nsub, c(int.mean, slope.mean), G)
sub.ints = sub.ints.slopes[,1]
time.slopes = sub.ints.slopes[,2]
# Step 2: Use the intercepts and slopes to generate RT data
sigma = 30
for (i in 1:nsub){
rt.vec = sub.ints[i] + time.slopes[i]*time + rnorm(length(time), sd =
sigma)
rt = c(rt, rt.vec)
}
dat = data.frame(rt, time.all, subid.all)
return(dat)
}
#Here I run one random-effects model
set.seed(10)
dat = dtfunc(16)
lmer(rt~time.all + (1+time.all |subid.all), dat)
Assuming the test for significance is likelihood ratio test and so in the
end, I want to see if I run the test 1000 times, what is the probability of
rejecting null hypothesis when it is TRUE. Also, how do I plot the behavior
of type I error if I change the values of standard deviations?
Any help is appreciated!
Best,
Chao
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