Dear R community, I am trying to understand how the predict function, specifically, the predict.loess function works. I understand that the loess function calculates regression parameters at each data point in 'data'. lo <- loess ( y~x, data) p <- predict (lo, newdata) I understand that the predict function predicts values for 'newdata' according to the loess regression parameters. How does predict.loess do this in the case that 'newdata' is different from the original data x? How does the interpolation take place? Thank you. [[alternative HTML version deleted]]
On Apr 3, 2012, at 4:12 PM, Recher She wrote:> Dear R community, > > I am trying to understand how the predict function, specifically, the > predict.loess function works. > > I understand that the loess function calculates regression > parameters at > each data point in 'data'. > > lo <- loess ( y~x, data)Well, it produces a fitted-value at each point. Whether there are parameters at each point might depend on the degree of the fit.> > p <- predict (lo, newdata) > > I understand that the predict function predicts values for 'newdata' > according to the loess regression parameters. How does predict.loess > do > this in the case that 'newdata' is different from the original data > x? How > does the interpolation take place?Type this at your console: getAnywhere(predict.loess) And after seeing that an additional functions is called type this: getAnywhere(predLoess) And then you will see that you have descended into a C function called 'C_loess_dfitse'.> > [[alternative HTML version deleted]]-- David Winsemius, MD West Hartford, CT
Run the examples for the "loess.demo" function in the TeachingDemos package to get a better understanding of what goes into the loess predictions. On Tue, Apr 3, 2012 at 2:12 PM, Recher She <rrrecher.she at gmail.com> wrote:> Dear R community, > > I am trying to understand how the predict function, specifically, the > predict.loess function works. > > I understand that the loess function calculates regression parameters at > each data point in 'data'. > > lo <- loess ( y~x, data) > > p <- predict (lo, newdata) > > I understand that the predict function predicts values for 'newdata' > according to the loess regression parameters. How does predict.loess do > this in the case that 'newdata' is different from the original data x? How > does the interpolation take place? > > Thank you. > > ? ? ? ?[[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.-- Gregory (Greg) L. Snow Ph.D. 538280 at gmail.com