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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