On Tue, 17 Feb 2009, 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 you try
lapply(1:9, function(i)quantile(mydata, pctile,type=i))
the answers from type=1 or 2 agree with rq().
-thomas
Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
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