On May 22, 2013, at 7:44 PM, meng wrote: > It's not homework. > I met this question during my practical work via R. > The boss is an expert of biology,but he doesn't know statistics.So I must > find the right method to this work. >
Yes, you must. Unfortunately, the Rhelp mailing list is for problem with R coding, but _not_ designed to offer tutorials on the proper education of stats-challenged biologists. It is an unfortunate truth that many a physician or biologist may rise to a position of authority without a proper grounding in statistics. The rectification of those deficiencies is not the stated goal of R help. -- David Winsemius, MD, MPH > > > > > > > > At 2013-05-22 17:30:34,"Uwe Ligges" <lig...@statistik.tu-dortmund.de> wrote: > > > > > >On 22.05.2013 07:09, meng wrote: > >> Thanks. > >> > >> > >> As to the data " warpbreaks", if I want to analysis the impact of > >> tension(L,M,H) on breaks, should I order the tension or not? > > > >No homework questions on this list, please ask your teacher. > > > >Best, > >Uwe Ligges > > > > > > > > > > > >> > >> > >> Many thanks. > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> At 2013-05-21 20:55:18,"David Winsemius" <dwinsem...@comcast.net> wrote: > >>> > >>> On May 20, 2013, at 10:35 PM, meng wrote: > >>> > >>>> Hi all: > >>>> If the explainary variables are ordinal,the result of regression is > >>>> different from > >>>> "unordered variables".But I can't understand the result of regression > >>>> from "ordered > >>>> variable". > >>>> > >>>> The data is warpbreaks,which belongs to R. > >>>> > >>>> If I use the "unordered variable"(tension):Levels: L M H > >>>> The result is easy to understand: > >>>> Estimate Std. Error t value Pr(>|t|) > >>>> (Intercept) 36.39 2.80 12.995 < 2e-16 *** > >>>> tensionM -10.00 3.96 -2.525 0.014717 * > >>>> tensionH -14.72 3.96 -3.718 0.000501 *** > >>>> > >>>> If I use the "ordered variable"(tension):Levels: L < M < H > >>>> I don't know how to explain the result: > >>>> Estimate Std. Error t value Pr(>|t|) > >>>> (Intercept) 28.148 1.617 17.410 < 2e-16 *** > >>>> tension.L -10.410 2.800 -3.718 0.000501 *** > >>>> tension.Q 2.155 2.800 0.769 0.445182 > >>>> > >>>> What's "tension.L" and "tension.Q" stands for?And how to explain the > >>>> result then? > >>> > >>> Ordered factors are handled by the R regression mechanism with orthogonal > >>> polynomial contrasts: ".L" for linear and ".Q" for quadratic. If the term > >>> had 4 levels there would also have been a ".C" (cubic) term. Treatment > >>> contrasts are used for unordered factors. Generally one would want to do > >>> predictions for explanations of the results. Trying to explain the > >>> individual coefficient values from polynomial contrasts is similar to and > >>> just as unproductive as trying to explain the individual coefficients > >>> involving interaction terms. > >>> > >>> -- > >>> > >>> David Winsemius > >>> Alameda, CA, USA > >>> > >> > >> [[alternative HTML version deleted]] > >> > >> ______________________________________________ > >> R-help@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. > >> > > > David Winsemius Alameda, CA, USA ______________________________________________ R-help@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.
