Hola!

Experimenting a little, package fda seems very much under development, and help pages are definitely not finished.
It would certainly help to read the book "functional data analysis" by the author of fda, which is in our library.
On the other hand package mgcv is more mature, so it would seem easier to use that.


It is not totally automatic, but the example
library(mgcv)
example(mono.con)

has everything necessary to get started.

Kjetil Halvorsen

Eliyahu-Oron wrote:

Kjetil and Andy,

Thanks for your helpful answers! The first two (mgcv and fda) seem to be in
the direction I'm looking for. I downloaded them both.

I'm running into a lot of implementation difficulties, though. I wonder if
there's anyone who tried to do a monotone spline using either the 'mgcv' or
the 'fda' packages, whom I could ask directly?

Thanks again, Assaf

-----Original Message-----
From: Kjetil Halvorsen [mailto:[EMAIL PROTECTED]
Sent: Monday, July 12, 2004 4:32 PM
To: Assaf P Oron
Cc: [EMAIL PROTECTED]
Subject: Re: [R] Smooth monotone estimation on R


help.search() on my machine turns up only:

mono.con(mgcv)          Monotonicity constraints for a cubic
                       regression spline.

smooth.monotone(fda)    Monotone Smoothing of Data
pmreg(ftnonpar)         Piecewise monotone regression with taut
                       strings
backSpline(splines)     Monotone Inverse Spline
isoreg(stats)           Isotonic / Monotone Regression

so you should find something of use in packages mgvc, fda, ftnonpar, splines
or stats (.loaded by default)

Kjetil Halvorsen


Assaf P Oron wrote:



Hi all,

I'm looking for smooth monotone estimation packages, preferably using


splines.


I downloaded the 'cobs' package and intend to use it, but since it offers


only quadratic splines based on L1 minimization, I'd like to compare its
performance to that of a more 'mainstream' cubic-spline, L2-norm minimizing
spline. Preferably a smoothing spline.


Does anyone know of such code existing anywhere? Or another smooth monotone


alternative?


Thanks in advance,

Assaf Oron
Statistics Department
University of Washington

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