Thanks for pointing me to the quantreg package as a resource.  I was hoping
to ask be able to address one quick follow-up question...

I get slightly different variants between using the rq funciton with formula
= mydata ~ 1 as I would if I ran the same data using the quantile function.

Example:

mydata <- (1:10)^2/2
pctile <- seq(.59, .99, .1)

quantile(mydata, pctile)
59%    69%    79%    89%    99%
20.015 26.075 32.935 40.595 49.145

rq(mydata~1, tau=pctile)
Call:
rq(formula = mydata ~ 1, tau = pctile)
Coefficients:
            tau= 0.59 tau= 0.69 tau= 0.79 tau= 0.89 tau= 0.99
(Intercept)        18      24.5        32      40.5        50
Degrees of freedom: 10 total; 9 residual

Is it correct to assume this is due to the different accepted methods of
calculating quantiles?  If so, do you know where I would be able to see the
algorithms used in these functions?  I'm not finding it in the documentation
for function rq, and am new enough to R that I don't know where those
references would generally be.




On Tue, Feb 17, 2009 at 12:29 PM, roger koenker <rkoenker@uiuc.edu> wrote:

> http://www.nabble.com/weighted-quantiles-to19864562.html#a19865869
>
> gives one possibility...
>
> url:    www.econ.uiuc.edu/~roger            Roger Koenker
> email    rkoenker@uiuc.edu            Department of Economics
> vox:     217-333-4558                University of Illinois
> fax:       217-244-6678                Champaign, IL 61820
>
>
>
>
> On Feb 17, 2009, at 10:57 AM, Brigid Mooney wrote:
>
>   Hi All,
>>
>> I am looking at applications of percentiles to time sequenced data.  I had
>> just been using the quantile function to get percentiles over various
>> periods, but am more interested in if there is an accepted (and/or
>> R-implemented) method to apply weighting to the data so as to weigh recent
>> data more heavily.
>>
>> I wrote the following function, but it seems quite inefficient, and not
>> really very flexible in its applications - so if anyone has any
>> suggestions
>> on how to look at quantiles/percentiles within R while also using a
>> weighting schema, I would be very interested.
>>
>> Note - this function supposes the data in X is time-sequenced, with the
>> most
>> recent (and thus heaviest weighted) data at the end of the vector
>>
>> WtPercentile <- function(X=rnorm(100), pctile=seq(.1,1,.1))
>> {
>>  Xprime <- NA
>>
>>  for(i in 1:length(X))
>>  {
>>   Xprime <- c(Xprime, rep(X[i], times=i))
>>  }
>>
>>  print("Percentiles:")
>>  print(quantile(X, pctile))
>>  print("Weighted:")
>>  print(Xprime)
>>  print("Weighted Percentiles:")
>>  print(quantile(Xprime, pctile, na.rm=TRUE))
>> }
>>
>> WtPercentile(1:10)
>> WtPercentile(rnorm(10))
>>
>>        [[alternative HTML version deleted]]
>>
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>> PLEASE do read the posting guide
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>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>

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