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RE: st: RE: How to define shortest possible period with 95% of observations


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: How to define shortest possible period with 95% of observations
Date   Wed, 12 May 2010 16:44:18 +0100

Without looking at this in detail, it seems to me that you might benefit
from thinking in terms of fire years, rather than calendar years,
starting on some day other than January 1.  After all, all sorts of
different sciences, not to mention religions, have years that don't
coincide with conventional Western calendar years: fiscal years, water
years, academic years, etc., etc. 

Several pertinent -egen- functions are included in -egenmore- on SSC. 

In other words, define the time scale in terms of those fire years; then
Robert's code will probably not need any complicated adjustments. 

Nick 
n.j.cox@durham.ac.uk 

Daniel Mueller

Robert, this works like charm!!! Thanks a bunch for this neat code. Also

thanks to Nick for pointing me to -shorth- which I will certainly 
explore in more detail after having sipped through the extensive 
reference list.

Using Roberts code I can seamlessly loop over the nine years of data and

generate the shortest fire season per year with 95% of obs. The results 
suggested an additional complication.. For some subsets the shortest 
possible period likely starts a couple of days before Jan 1st, at the 
end of the preceding year.

I tweaked Roberts code a little to loop over years and defined the 
middle of a year as the peak fire day. The code runs through, yet sets 
the start of the fire season for some subsets to Jan 1st, while my 
educated guess is that it should be somewhere around mid to end of 
December. Something went wrong, but I can't spot the glitch in the code 
below. Can someone please help?

Thanks a lot in advance and best regards,
Daniel


*** start
forv y = `yearfirst'/`yearlast' {

* keep previous year
  if `y' != `yearfirst' {
   keep if Year == `y' | Year == (`y'-1)
  }
  bys Day: g no_fire_day = _N
  qui su no_fire_day

* define year to start 183 days before peak fire day
  loc yearstart = Day[r(max)] - 183
  loc yearend = `yearstart' + 365
  keep if Day > `yearstart' & Day < `yearend' // or with egen->rotate?
  bys Day: keep if _n == _N
  g nobs = _n

* the target is a continuous run that includes 95% of all fires
  sum no_fire_day, meanonly
  scalar target = .95 * r(sum)

  scalar shortlen = .
  gen arun = .
  gen bestrun = .

  * at each pass, create a run that starts at nobs == `i'
  * and identify the nobs where the number of fires >= 95%
  local more 1
  local i = 0
  while `more' {
   local i = `i' + 1
   qui replace arun = sum(no_fire_day * (nobs >= `i'))
   sum nobs if arun >= target, meanonly
   if r(N) == 0 local more 0
   else if (Day[r(min)] - Day[`i']) < shortlen {
    scalar shortlen = Day[r(min)] - Day[`i']
    qui replace bestrun = arun
    qui replace bestrun = . if nobs > r(min) | nobs < `i'
   }
  }
  qui drop if bestrun == .
  drop bestrun arun
  save fires_`y', replace
}
*** end





Robert Picard wrote on 5/11/2010 3:28 AM:
> Here is how I would approach this problem. I would do each year
> separately; it could be done all at once but it would complicate the
> code unnecessarily. If the fire data is one observation per fire, I
> would -collapse- it to one observation per day. Each observation would
> contain the number of fires that day. The following code will identify
> the first instance of the shortest run of days that includes 95% of
> fires for the year.
>
> Note that the following code will work, even if there are days without
> fires (and thus no observation for that day).
>
> *--------------------------- begin example -----------------------
> version 11
>
> * daily fire counts; with some days without fires
> clear all
> set seed 123
> set obs 365
> gen day = _n
> drop if uniform()<  .1
> gen nobs = _n
> gen nfires = round(uniform() * 10)
>
> * the target is a continuous run that includes 95% of all fires
> sum nfires, meanonly
> scalar target = .95 * r(sum)
> dis target
>
> scalar shortlen = .
> gen arun = .
> gen bestrun = .
>
> * at each pass, create a run that starts at nobs == `i'
> * and identify the nobs where the number of fires>= 95%
> local more 1
> local i 0
> while `more' {
> 	local i = `i' + 1
> 	qui replace arun = sum(nfires * (nobs>=`i'))
> 	sum nobs if arun>= target, meanonly
> 	if r(N) == 0 local more 0
> 	else if (day[r(min)] - day[`i'])<  shortlen {
> 		scalar shortlen = day[r(min)] - day[`i']
> 		qui replace bestrun = arun
> 		qui replace bestrun = . if nobs>  r(min) | nobs<  `i'
> 	}
> }
>
> *--------------------- end example --------------------------
>
>
> Hope this help,
>
> Robert
>
> On Mon, May 10, 2010 at 6:19 AM, Nick Cox<n.j.cox@durham.ac.uk>
wrote:
>> I don't think any trick is possible unless you know in advance the
>> precise distribution, e.g. that it is Gaussian, or exponential, or
>> whatever, which here is not the case.
>>
>> So, you need to look at all the possibilities from the interval
starting
>> at the minimum to the interval starting at the 5% point of the fire
>> number distribution in each year.
>>
>> However, this may all be achievable using -shorth- (SSC). Look at the
>> -proportion()- option, but you would need to -expand- first to get a
>> separate observation for each fire. If that's not practicable, look
>> inside the code of -shorth- to get ideas on how to proceed. Note that
no
>> looping is necessary: the whole problem will reduce to use of -by:-
and
>> subscripts.
>>
>> Nick
>> n.j.cox@durham.ac.uk
>>
>> Daniel Mueller
>>
>> I have a strongly unbalanced panel with 100,000 observations (=fire
>> occurrences per day) that contain between none (no fire) and 3,000
fires
>>
>> per day for 8 years. The fire events peak in March and April with
about
>> 85-90% of the yearly total.
>>
>> My question is how I can define the shortest possible continuous
period
>> of days for each year that contains 95% of all yearly fires. The
length
>> and width of the periods may slightly differ across the years due to
>> climate and other parameters.
>>
>> I am sure there is a neat trick in Stata for this, yet I have not
>> spotted it. Any suggestions would be appreciated.

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