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Re: st: Trying to simulate sampling distribution of mean


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Trying to simulate sampling distribution of mean
Date   Wed, 30 Jan 2013 01:30:13 +0000

If you want to do it this way, you can simplify your program

program ybar
         qui use big.dta, clear
         sample 60, count
         su age, meanonly
end

I think that should still work. -syntax- does nothing for you.
-summarize- leaves r(mean) in its wake any way. Taking a variable and
putting it in another and taking a saved result and putting it in
another can both be excised.

Nick


On Wed, Jan 30, 2013 at 12:04 AM, krishanu karmakar
<krishkarmakar@gmail.com> wrote:
> Thank you Dr. Cox,
>
> I did a little bit more searching and with the help of your answer I
> modified my -ybar- program as follows
>
> -----------------------------
> program define ybar, rclass
>         syntax [,]
>         qui use big.dta, clear
>         sample 60, count
>         gen y1 = age
>         summ y1
>         return scalar my = r(mean)
> end
>
> local reps 5
> simulate rmy=r(my), saving(sdistmean`i', replace) nodots reps(`reps'): ybar
> -----------------------------------
> yes, I should probably put the -use- command as an option to the
> -ybar- program to make it more generally usable. But, otherwise, it is
> now working as i wanted it to.
>
> Thank you again.
> Krishanu
>
>
> On Tue, Jan 29, 2013 at 6:51 PM, Nick Cox <njcoxstata@gmail.com> wrote:
>> Your program -ybar- does exactly the same thing every time, so
>> inevitably the results are the same. If you look again at the help for
>> -simulate- you will see that the example program -lnsim- includes its
>> own random variate generation. Conversely, you do use -sample 0.1- but
>> you use it outside your program.
>>
>> Otherwise put, -simulate- does not actually do stochastic simulation;
>> it is just a framework that runs and collates the results of a program
>> you write -- and that program must do the simulation
>>
>> In your case, there is an easy way of getting random samples from your
>> dataset. Just chop the dataset into blocks randomly and summarize each
>> block. .
>>
>> If you shuffle your data
>>
>> set seed 2803
>> gen random = runiform()
>> sort random
>>
>> and create blocks of size 100
>>
>> gen block = ceil(_n/100)
>>
>> then
>>
>> egen mean = mean(age), by(block)
>> egen tag = tag(block)
>> l mean if tag
>>
>> that will give you 1000 means each for blocks of size 100. For some
>> reason, it seems that you only want 5, and that means you can throw
>> 995 away.
>>
>> Nick
>>
>> On Tue, Jan 29, 2013 at 11:15 PM, krishanu karmakar
>> <krishkarmakar@gmail.com> wrote:
>>
>>> The following is my code
>>>
>>> ==== code start =====
>>>
>>> program define ybar, rclass
>>>         syntax [,]
>>>         replace y1 = y2
>>>         summarize y1
>>>         return scalar m_y = r(mean)
>>> end
>>>
>>>
>>> local reps 5
>>>
>>>         quietly use big.dta, clear
>>>         generate y2 = age
>>>         sample 0.1
>>>
>>>         quietly{
>>>         gen y1=.
>>>         simulate m_age=r(m_y), saving(meandata, replace) nodots reps(`reps'): ybar
>>> }
>>>
>>> ==== code ends =====
>>>
>>> What I am trying to do.
>>> I have a dataset named "big.dta" with 100,000 observations. The only
>>> variable in this dataset is "age".
>>>
>>> I want to first draw a sample of size 100 from this dataset and
>>> calculate the mean for the variable "age". I want to draw 5 such
>>> samples and store the mean of "age" from each sample as the variable
>>> "m_age" in a new dataset called "meandata". So this dataset will have
>>> 5 observations.
>>>
>>> My code is running, but wrongly. I am getting stata to save the
>>> "meandata", but all the five observations (mean of age from 5
>>> different samples) are stored as equal in value. That means stata is
>>> not drawing 5 different samples, but only one sample. Could anyone
>>> help by showing which line my code should I change?
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