
Dear list, 
  I have a dataset containing values obtained from two different instruments (x and y).
I want to generate 5 samples from normal distribution for each instrument based on 
their means and standard deviations. The problem is values from both instruments are 
non-negative, so if using rnorm I would get some negative values. Is there any options
to determine the lower bound of normal distribution to be 0 or can I simulate the 
samples in different ways to avoid the negative values? 
   
   
  > dat
    id     x         y
75 101 0.134 0.1911315
79 102 0.170 0.1610306
76 103 0.134 0.1911315
84 104 0.170 0.1610306
74 105 0.134 0.1911315
80 106 0.170 0.1610306
77 107 0.134 0.1911315
81 108 0.170 0.1610306
82 109 0.170 0.1610306
78 111 0.170 0.1610306
83 112 0.170 0.1610306
85 113 0.097 0.2777778
2  201 1.032 1.5510434
1  202 0.803 1.0631001
5  203 1.032 1.5510434
  
mu<-apply(dat[,-1],2,mean)
sigma<-apply(dat[,-1],2,sd)
  len<-5
n<-20
s1<-vector("list",len)
  set.seed(7)
for(i in 1:len){
    s1[[i]]<-cbind.data.frame(x=rnorm(n*i,mean=mu[1],sd=sigma[1]),
                             y=rnorm(n*i,mean=mu[2],sd=sigma[2]))
               }
     
Thanks for any help,
Tom

       
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