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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Plotting a Local Polynomial Regression with CIs Accounting for Clustering |

Date |
Tue, 1 Dec 2009 12:35:48 -0000 |

I don't get a good sense of the statistical strategy here. You mix statistical (and perhaps scientific) judgment in choosing what is qualitatively correct and a wish to quantify that exactly with confidence intervals that pay attention to clustering. The latter exactness seems dubious in light of the former inexactness. I wonder how that all is to be reported. I suppose we all do something like this much of the time, however. It seems that there is only one predictor. Given, that a scatter plot with data and smooth goes most of the way to conveying variability around the smooth. In addition, I don't see why you couldn't just use -mkspline- to get cubic splines and then use -regress- directly on the created variables. -mkspline- only allows frequency weights but as long as you use -regress- with weights as you wish you should get something like what you want. Nick n.j.cox@durham.ac.uk L S I've been playing around with the fracpoly graphs for a couple days now. Compared to the local polynomial regression lines, they do not look quite right. The main thing is that the picture will depend often depend fairly strongly on the number of degrees for the fractional polynomial. If you specify a number too small, the graph will appear oversmoothed. If you specify a number of degrees too large, then the 95% CIs will often get very large. fracpoly reg y x, cluster(id) degree(2) fracplot, msymbol(none) addplot((function y=x)) fracpoly reg y x, cluster(id) degree(6) fracplot, msymbol(none) addplot((function y=x)) In the toy data above this is not so bad, but it is more of an issue with the real data. I realize that choosing the degrees is a necesary choice. It seems though that lpoly (local polynomial) regression produces a graph for my data that seems more reasonable. Thus, though I said I was flexible with respect to which form of nonparametric regression is used, I was wondering if there might be a way to possibly return back to local polynomial regression or perhaps another form of nonparametric regression (besides fracpoly) that will allow me to plot 95% CIs accounting for clustering, e.g. something like twoway (lpolyci y x, cluster(id)) (line x x) * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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