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