how to skip and break a loop in R - r

Question

I am trying write a function that generates simulated data but if the simulated data does not meet the condition, I need to skip it and if it does meet the condition, then I will apply the function summary.
I would like to loop it until I find 10 valid datasets and then stop. (I actually have to do this until it reaches 10000). Here is the code. The code sort of works except it does not stop. I think I probably placed the next and break function in the wrong place. I hope someone could help me write this together.
Another way I could approach this is to generate all the valid data first and then apply the function find_MLE (summary) to the final list.
Edit: I put break inside repeat. I edit the code to make it reproducible. Still the code keeps generating data and does not break.
here is a reproducible version
validData <- function(GM, GSD,sampleSize, p) {
count=0
for (i in 1:n) {
repeat {
lod <- quantile(rlnorm(1000000, log(GM), log(GSD)), p = p)
X_before <- rlnorm(sampleSize, log(GM), log(GSD))
Xs <- ifelse(X_before <= lod, lod, X_before)
delta <- ifelse(X_before <= lod, 1, 0)
pct_cens <- sum(delta)/length(delta)
print(pct_cens)
if (pct_cens == 0 & pct_cens ==1) next
else {
sumStats <- summary(Xs)
Med <- sumStats[3]
Ave <- sumStats[4]
}
count<- count+1
if (count == 10) break
}}
return(c(pct_cens, Med, Ave))
}
validData(GM=1,GSD=2,sampleSize=10,p=0.1)

Answer 1

Thanks for your help. I was able to write a function without using break function! I posted it here in case other people might find it helpful.
dset <- function (GM, GSD, n, p) {
Mean <- array()
Median <- array()
count = 0
while(count < 10) {
lod <- quantile(rlnorm(1000000, log(GM), log(GSD)), p = p)
X_before <- rlnorm(n, log(GM), log(GSD))
Xs <- ifelse(X_before <= lod, lod, X_before)
delta <- ifelse(X_before <= lod, 1, 0)
pct_cens <- sum(delta)/length(delta)
print(pct_cens)
if (pct_cens == 0 | pct_cens == 1 ) next
else {count <- count +1
if (pct_cens > 0 & pct_cens < 1) {
sumStats <- summary(Xs)
Median[count] <- sumStats[3]
Mean [count]<- sumStats[4]
print(list(pct_cens=pct_cens,Xs=Xs, delta=delta, Median=Median,Mean=Mean))
}
}
}
return(data.frame( Mean=Mean, Median=Median)) }

Answer 2

Since your code isn't replicable, I cannot fully test and debug your code, but here is what I think it would look like without being able to replicate with an MLE function. This is roughly how I would set it up. But check out the documentation/Google on break, next, for/while loops related to R when testing your code.
validData <- function(GM, GSD,Size, p) {
for (i in 1:20) {
count <- 1
repeat {
lod <- quantile(rlnorm(1000000, log(GM), log(GSD)), p = p)
X_before <- rlnorm(Size, log(GM), log(GSD))
Xs <- ifelse(X_before <= lod, lod, X_before)
delta <- ifelse(X_before <= lod, 1, 0)
pct_cens <- sum(delta)/length(delta)
if (pct_cens == 0 & pct_cens ==1)
function() #your foo goes here
else {
mles <- find_MLE(c(0,0), Xs, delta)
GM_est <- mles[1]
GSD_est <- mles[2]
AM_est <- exp(log(GM_est) + 1 )
SD_est<- sqrt((AM_est)^2*exp(log(GSD_est)^2))
D95th_est <- GM_est*(GSD_est^1.645)
} }
return(c(GM_est,GSD_est,AM_est,SD_est,D95th_est))
count<- count+1
if (count == 10) break
}}

Answer 3

To skip to the outer loop based on a condition, simply use break()
Here's a simple example where the inner loop will try to run 10 times, but a condition will usually be met which prevents it
# OUTER LOOP
for(i in 1:2) {
print(paste("Outer loop iteration", i))
# INNER LOOP (will run max 10 times)
for(j in 1:10) {
print(paste("Inner loop iteration", j))
if (runif(1) > 0.4) { # Randomly break the inner loop
print(paste("Breaking inner loop", j))
break()
}
}
}
If you want to skip to the outer loop when there's an error (rather than based on a condition), see here