[R] trouble with exiting loop if condition is met
MacQueen, Don
m@cqueen1 @end|ng |rom ||n|@gov
Thu Jun 28 23:45:52 CEST 2018
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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