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 1:58 PM, Brigid Mooney wrote:
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.
Yes, quantile() in base R documents 9 varieties of quantiles, 2 more
than
William Empson's famous 7 Types of Ambiguity. In quantreg the function
rq() finds a solution to an underlying optimization problem and
doesn't ask
any further into the nature of the ambiguity -- it does often produce a
warning indicating that there may be more than one solution. The
default
base R quantile is interpolated, while the default rq() with method =
"br"
using the simplex algorithm finds an order statistic, typically. If
you prefer
something more like interpolation, you can try rq() with method = "fn"
which is using an interior point algorithm and when there are multiple
solutions it tends to produce something more like the centroid of the
solution set. I hope that this helps.
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))
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