From: James McDermott <jp.mcdermott_at_gmail.com>

Date: Wed 20 Jul 2005 - 05:34:52 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Jul 20 05:43:21 2005

Date: Wed 20 Jul 2005 - 05:34:52 EST

I wish it were that simple (perhaps it is and I am just not seeing it). The output from cobs( ) includes the B-spline coefficients and the knots. These coefficients are not the same as the a, b, and c coefficients in a quadratic polynomial. Rather, they are the coefficients of the quadratic B-spline representation of the fitted curve. I need to evaluate a linear combination of basis functions and it is not clear to me how to accomplish this easily. I was hoping to find an alternative way of getting the derivatives.

Jim McDermott

On 7/19/05, Duncan Murdoch <murdoch@stats.uwo.ca> wrote:

> On 7/19/2005 2:53 PM, James McDermott wrote:

*> > Hello,
**> >
**> > I have been trying to take the derivative of a quadratic B-spline
**> > obtained by using the COBS library. What I would like to do is
**> > similar to what one can do by using
**> >
**> > fit<-smooth.spline(cdf)
**> > xx<-seq(-10,10,.1)
**> > predict(fit, xx, deriv = 1)
**> >
**> > The goal is to fit the spline to data that is approximating a
**> > cumulative distribution function (e.g. in my example, cdf is a
**> > 2-column matrix with x values in column 1 and the estimate of the cdf
**> > evaluated at x in column 2) and then take the first derivative over a
**> > range of values to get density estimates.
**> >
**> > The reason I don't want to use smooth.spline is that there is no way
**> > to impose constraints (e.g. >=0, <=1, and monotonicity) as there is
**> > with COBS. However, since COBS doesn't have the 'deriv =' option, the
**> > only way I can think of doing it with COBS is to evaluate the
**> > derivatives numerically.
**>
**> Numerical estimates of the derivatives of a quadratic should be easy to
**> obtain accurately. For example, if the quadratic ax^2 + bx + c is
**> defined on [-1, 1], then the derivative 2ax + b, has 2a = f(1) - f(0) +
**> f(-1), and b = (f(1) - f(-1))/2.
**>
**> You should be able to generalize this to the case where the spline is
**> quadratic between knots k1 and k2 pretty easily.
**>
**> Duncan Murdoch
**>
*

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Jul 20 05:43:21 2005

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