Hi Marietta,
You may not be aware that the variable k is doing nothing in your
example except running the random variable generation 2 or 3 times for
each cycle of the outer loop as each successive run just overwrites
the one before. If you want to include all two or three lots of values
you will have to do something like this:
db<-list()
gen_sample<-function(n,k) {
for(m in 1:length(n)) {
for(j in 1:n[m]) {
for(i in 1:k[m]) {
dbindx<-i+(j-1)*k[m]+(m-1)*(n[m]+k[1]+k[2])
db[[dbindx]]<-
matrix(c(rnorm(n[m], 100, 15),
rsnorm(n[m], 100, 15),
rlogis(n[m], 100, 15)),ncol=3)
}
}
}
return(db)
}
gen_sample(c(10,15),c(2,3))
Jim
On Sat, Jul 2, 2016 at 3:28 AM, Marietta Suarez <marietta0...@gmail.com> wrote:
> i'm trying to generate data for a meta analysis. 1- generate data following
> a normal distribution, 2- generate data following a skewed distribution, 3-
> generate data following a logistic distribution. i need to loop this
> because the # of studies in each meta will be either 10 or 15. k or total
> number of studies in the meta will be 5. i need to loop twice to repeat
> this process 10 times. database should be 3 columns (distributions) by 65
> rows x 10 reps
>
>
> here's my code, not sure what's not working:
> library(fGarch)
>
> #n reps =10
> rep=10
>
> #begin function here, need to vary n and k, when k=2 n=10, when k3 n=15
> fun=function(n, k){
>
> #prepare to store data
> data=matrix(0,nrow=10*k, ncol=3)
> db=matrix(0,nrow=650, ncol=3)
>
> for (j in 1:rep)
> {
> for (i in 1:k)
> {
> #generate data under normal, skewed, and logistic distributions here
>
> data[,1]=rnorm(n, 100, 15)
> data[,2]=rsnorm(n, 100, 15, 1)
> data[,3]=rlogis(n, 100, 15)
> }
> [j]=db
> }
> }
>
> save=fun(10,2)
>
> Please help!!!
>
> [[alternative HTML version deleted]]
>
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