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.
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. >> [[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.
