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# Re: st: distribution test

 From Nick Cox To statalist@hsphsun2.harvard.edu Subject Re: st: distribution test Date Tue, 30 Aug 2011 11:55:35 +0100

```I support this idea of using a portfolio of simulations for comparison.

A minor curiosity is to note that although the individual results of

runiform()

and

1 - runiform()

are only very exceptionally equal (when both are exactly 0.5) , the
underlying distributions are identical.  So subtracting from 1 does no
harm but is not needed here statistically.

Nick

On Tue, Aug 30, 2011 at 11:19 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> On Tue, Aug 30, 2011 at 11:13 AM, Nick Cox wrote:
>> There is a direct method to check for fit to an exponential
>> distribution: a quantile-quantile plot. See -qexp- (SSC) and/or
>>
>> SJ-7-2  gr0027  . .  Stata tip 47: Quantile-quantile plots without programming
>>        . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  N. J. Cox
>>        Q2/07   SJ 7(2):275--279                                 (no commands)
>>        tip on producing various quantile-quantile (Q-Q) plots
>>
>> on how to do it for yourself. The latter is directly accessible at
>
> I like to use estimated coefficients for computing the values on the
> x-axis, as that way the graph should be square and the reference line
> is the 45 degree line. Moreover,I like to supplement such a
> quantile-quantile plot with several random draws from the assumed
> distribution. This gives me an idea of how much deviation from the
> theoretical distribution I might reasonably expect. Below is an
> example of how I would do that.
>
> *--------------------- begin example --------------------
> // create some expnential data
> local lambda = 2
> drop _all
> set obs 500
> gen y = -1/`lambda'*ln(1-runiform())
> label var y "observed"
>
> // estimate parameter
> sum y, meanonly
> local lambdahat = 1/r(mean)
> di as txt "ML estimate of lambda is: " ///
>   as result `lambdahat'
>
> // will use that later for computing the range of the graph
> local max = r(max)
>
> // As discussed in: Nicholas J. Cox (2007) Stata tip 47:
> // Quantile-quantile plots without programming. The Stata
> // Journal, 7(2): 275--279.
> egen rank = rank(y)
> egen n = count(y)
> gen pp = (rank - 0.5) / n
> gen exponential = -1/`lambdahat'*ln(1 - pp)
>
> // will use that to compute the range of the reference line
> sum exponential, meanonly
> local reflinerange "range(0 `r(max)')"
>
> // create 20 random variables assuming the model is correct
> forvalues i = 1/20 {
>        gen y`i' = -1/`lambdahat'*ln(1-runiform())
>        egen rank`i' = rank(y`i')
>        egen n`i' = count(y`i')
>        gen pp`i' = (rank`i' - 0.5) / n`i'
>        gen exponential`i' = -1/`lambdahat'*ln(1 - pp`i')
>        drop rank`i' n`i'  pp`i'
>        #delim ;
>        local gr `"`gr' || line y`i' exponential`i',
>                    sort lstyle(solid) lcolor(gs12)"' ;
>        #delim cr
>        sum y`i', meanonly
>        local max = max(`r(max)', `max')
> }
>
> // compute nice axis labels
> _natscale 0 `max' 5
> local lab "lab(`r(min)'(`r(delta)')`r(max)')"
>
> // make sure the graph is square
> local range "scale(range(0 `max'))"
>
> // use var label for y-axis title when present
> if `"`: var label y'"' != "" {
>        local ytitle `"ytitle(`"`: var label y'"')"'
> }
> else {
>        local ytitle `"ytitle(y)"'
> }
>
> // create the graph
> twoway `gr'                            || ///
>       scatter y exponential,             ///
>       y`lab' x`lab' y`range' x`range'    ///
>       aspect(1) msymbol(oh)           || ///
>       function reference = x,            ///
>       `reflinerange' lstyle(solid)       ///
>       legend(order( 1 "samples" 21 22 )) ///
>       xtitle(exponential distribution)   ///
>       `ytitle'
> *---------------------- end example ---------------------
> (For more on examples I sent to the Statalist see:
> http://www.maartenbuis.nl/example_faq )
>
> Hope this helps,

*
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```