R: simulating 2-level model - r

Question

I am trying to simulate the unequal sample size in the multilevel model.I have four groups, the sample size is 100,200,300,and 400, respectively.
So, the total sample size is 1000. w, u0,u1 variables are in the level 2 ; x , r0 are in the level 1. y is an outcome
nSubWithinGroup <- c(100,200,300,400)###the sample size in each group
nGroup <-4 ## 4 groups
gamma00 <- 1
gamma01 <- 1 ## b0 = gamma00+gamma01*w+u0
gamma10 <- 1 ## b1 = gamma10+gamma11*w+u1
gamma11 <- 1
dataLevel1 <- mat.or.vec(sum(nSubWithinGroup),4)
colnames(dataLevel1) <- c("Group","X","W","Y")
rowIndex <- 0
for (group in 1:nGroup) {
u0 <- rnorm(1,mean=0,sd=1)
u1 <- rnorm(1,mean=0,sd=1)
w <- rnorm(1,mean=0,sd=1)
for(i in 1:length(nSubWithinGroup)){
for (j in 1:nSubWithinGroup[i]){
r0 <- rnorm(1,mean=0,sd=1)
x <- rnorm(1,mean=0,sd=1)
y <- (gamma00+gamma01*w+u0)+(gamma10+gamma11*w+u1)*x+r0
rowIndex <- rowIndex + 1
dataLevel1[rowIndex,] <- c(group,x,w,y)
}
}
}
I ran the codes, and it showed me the value in the "Group" column is 1 , no 2,3, or 4. Also, it has errors, which is:
"Error in [<-(*tmp*, rowIndex, , value = c(2, -1.94476463667851, -0.153516782293473, :
subscript out of bounds"

Answer 1

Your original issue was a bit hard to find with all the for-loops, but you were looping twice on your grouping level (one time in 1:nGroup and then again in 1:length(nSubWithinGroup). This lead to more combinations than you had allowed for in your matrix, and thus your error. (If you want to check, run your loop without assigining to dataLevel1 and see what value rowIndex has at the end.
However, generating data like this in R can be notoriously slow and every function you use with n=1 can just as easily be used to generate nTotal numbers. I have rewritten your code to something that's (hopefully) more readable, but also more vectorized.
#set seed; you can never reproduce your result if you don't do this
set.seed(289457)
#set constants
gamma00 <- 1
gamma01 <- 1 ## b0 = gamma00+gamma01*w+u0
gamma10 <- 1 ## b1 = gamma10+gamma11*w+u1
gamma11 <- 1
#set size parameters
nSubWithinGroup <- c(100,200,300,400)###the sample size in each group
nGroup <-4
nTotal <- sum(nSubWithinGroup)
#simulate group-level data
level2_data <- data.frame(group=1:nGroup,
size=nSubWithinGroup, #not really necessary here, but I like to have everything documented/accessible
u0 = rnorm(nGroup,mean=0,sd=1),
u1 = rnorm(nGroup,mean=0,sd=1),
w = rnorm(nGroup,mean=0,sd=1)
)
#simulate individual_level data (from example code x and r0 where generated in the same way for each individual)
level1_data <- data.frame(id=1:nTotal,
group=rep(1:nGroup, nSubWithinGroup),
r0 = rnorm(nTotal,mean=0,sd=1),
x = rnorm(nTotal, mean=0,sd=1)
)
#several possibilities here, you can merge the two dataframes together or reference the level2data when calculating the outcome
#merging generates more data, but is also readable
combined_data <- merge(level1_data,level2_data,by="group",all.x=T)
#calculate outcome. This can be shortened for instance by calculating some linear parts before
#merging but wanted to stay as close to original code as possible.
combined_data$y <- (gamma00+gamma01*combined_data$w+combined_data$u0)+
(gamma10+gamma11*combined_data$w+combined_data$u1)*combined_data$x+combined_data$r0