[R] trouble with exiting loop if condition is met

[MacQueen, Don]

Does this example help?

> for (ii in 1:10) { cat( ii,'\n') ; if (ii >3) break }
1 
2 
3 
4

The loop won't stop unless you tell it to stop, and I don't see any place where you told it to stop.

--
Don MacQueen
Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062
Lab cell 925-724-7509
 
 

On 6/28/18, 12:53 PM, "R-help on behalf of Kelly Wu" <r-help-bounces using r-project.org on behalf of kekwu using ucdavis.edu> wrote:

    I am working on a clinical trial simulation with two groups, treatment and
    placebo, and the outcome is dichotomous (recovery or no recovery) . I would
    like to stop my loop if either of my conditions are met:
    
    1) futility - there are no responses to treatment in the treatment group.
    2) the p-value is significant (<=0.01).
    
    The problem I am having is my loop continues to run 10,000 times even though
    I am sure that at least one of the conditions are met in some instances. It
    appears the main problem is that my if loop is not adequately filtering the
    conditions I specified in it.
    
    library(magrittr)
    library(dplyr)
    
    nSims <- 10000 #number of simulated experiments
    futility1 <-numeric(nSims) #set up empty container for all simulated
    futility
    futility2 <-numeric(nSims) #set up empty container for all simulated
    futility
    
    significant1 <-numeric(nSims) #set up empty container for all simulated
    significance
    significant2 <-numeric(nSims) #set up empty container for all simulated
    significance
    
    for(i in 1:nSims){ #for each simulated experiment
     # Year 1
    
     # p1<-response in controls
     # p2<-response in treated
     # Generating random deviates from a Uniform(0,1) distribution
     control.year1<-(runif(16, min = 0, max = 1))
     treat.year1<-(runif(16, min = 0, max = 1))
    
     #Generating dichotomous response variables for each group
     control.respond1<-ifelse(control.year1<=0.05,1,0)
     treat.respond1<-ifelse(treat.year1<=0.30,1,0)
    
     #Summing number of responses from each group
     control.no1<-sum(control.respond1==0)
     control.yes1<-sum(control.respond1==1)
     treat.no1<-sum(treat.respond1==0)
     treat.yes1<-sum(treat.respond1==1)
    
     #Perform the Fisher's exact test (one sided) with p<0.01
    
    fisher<-matrix(c(control.no1,control.yes1,treat.no1,treat.yes1),nrow=2,ncol=2)
     f<-fisher.test(fisher,alternative = "greater") 
    
     #year 2
     if (f$p.value>0.01 && treat.yes1!=0){
       # Generating random deviates from a Uniform(0,1) distribution
       control.year2<-(runif(16, min = 0, max = 1))
       treat.year2<-(runif(16, min = 0, max = 1))
    
       #Generating dichotomous response variables for each group
       control.respond2<-ifelse(control.year2<=0.05,1,0)
       treat.respond2<-ifelse(treat.year2<=0.30,1,0)
    
       #Summing number of responses from each group
       control.no2<-sum(control.respond2==0)
       control.yes2<-sum(control.respond2==1)
       treat.no2<-sum(treat.respond2==0)
       treat.yes2<-sum(treat.respond2==1)
    
       #Perform the Fisher's exact test (one sided) with p<0.01
    
    fisher2<-matrix(c(control.no2,control.yes2,treat.no2,treat.yes2),nrow=2,ncol=2)
       f2<-fisher.test(fisher2,alternative = "greater") 
     }
    
    
     significant2[i]<-ifelse(f2$p.value<0.01,1,0)
     futility2[i]<-ifelse(treat.yes2==0,1,0)
    }
    
    table(significant1)
    table(futility1)
    
    table(significant2)
    table(futility2)
    
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