Introduction to Stata 8 - (GRIPS

15.4. Random samples, simulations
Random number functions
Stata can create 'pseudo-random' numbers:
[R] Functions
gen y=uniform() Uniformly distributed in the interval 0-1
gen y=invnorm(uniform()) Normal distribution, mean=0, SD=1
gen y=10+2*invnorm(uniform()) Normal distribution, mean=10, SD=2
If you run the same command twice it will yield different numbers. If you need to reproduce
the same series of 'random' numbers, initialize the seed (a large integer used for the initial
calculations):
set seed 654321
Random samples and randomization
You may use sample to select a random sample of your data set:
sample 10
Selects an approximately 10 percent random sample.
sample 53 , count Selects exactly 53 observations at random.
You may assign observations randomly to two treatments:
generate y=uniform()
generate treat=1
replace treat=2 if y>0.5
And you may sort your observations in random sequence:
generate y=uniform()
sort y
Generating artifical data sets
You may use set obs to create empty observations. The following sequence defines a file
with 10,000 observations, used to study the behaviour of the difference (dif) between two
measurements (x1, x2), given information about components of variance (sdwithin,
sdbetw).
set obs 10000
generate sdbetw = 20
generate sdwithin = 10
generate sdtotal = sqrt(sdbetw^2 + sdwithin^2)
generate x0 = 50 + sdbetw*invnorm(uniform())
generate x1 = x0 + sdwithin*invnorm(uniform())
generate x2 = x0 + sdwithin*invnorm(uniform())
generate dif = x2 - x1
summarize
See another example in section 14.7 (the twoway rspike graph)
Advanced simulations
With simulate you may set up quite complex Monte Carlo simulations.
[R] simulate
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