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. Regards, Jim McDermott ______________________________________________ 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
