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?
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
>
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