Hi,
thanks, it works :-)
But where is the difference between demand ~ Time + I(Time^2) and demand ~
poly(Time, 2) ?
Or: How do I have to interpret the results? (I get different results for the
two methods)
Thank you again!
Gabor Grothendieck wrote:
Those are linear in the coefficients so try these:
library(quantreg)
rq1 - rq(demand ~ Time + I(Time^2), data = BOD, tau= 1:3/4); rq1
# or
rq2 - rq(demand ~ poly(Time, 2), data = BOD, tau = 1:3/4); rq2
On Tue, Jun 9, 2009 at 10:55 AM, despairedmeyfa...@uni-potsdam.de wrote:
Hi,
I'm relatively new to R and need to do a quantile regression. Linear
quantile regression works, but for my data I need some quadratic
function.
So I guess, I have to use a nonlinear quantile regression. I tried the
example on the help page for nlrq with my data and it worked. But the
example there was with a SSlogis model. Trying to write
dat.nlrq - nlrq(BM ~ I(Regen100^2), data=dat, tau=0.25, trace=TRUE)
or
dat.nlrq - nlrq(BM ~ poly(Regen100^2), data=dat, tau=0.25, trace=TRUE)
(I don't know the difference) both gave me the following error message:
error in getInitial.default(func, data, mCall = as.list(match.call(func,
:
no 'getInitial' method found for function objects
Looking in getInitial, it must have to do something with the starting
parameters or selfStart model. But I have no idea, what this is and how I
handle this problem. Can anyone please help?
Thanks a lot in advance!
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