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 <rkoen...@uiuc.edu> wrote:
> http://www.nabble.com/weighted-quantiles-to19864562.html#a19865869
>
> gives one possibility...
>
> url: www.econ.uiuc.edu/~roger Roger Koenker
> email rkoen...@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]]
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html>
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
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