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Aug 27, 2014 2:34 PM
(1840 views)

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Aug 28, 2014 11:36 AM
(3396 views)

Solution

This is typical for any regression when you have a categorical input variable. The parameter estimates for each level of a categorical input are "offsets" from the overall mean. This leads to a restriction that the sum of the parameter estimates for each level is zero. Because of this, if you have k-levels of a categorical factor you only need k-1 parameter estimates. The estimate for the last level is -1 times the sum of the other parameter estimates. For example, suppose your estimates for habitat type are -6 and 12 for levels A and B respectively. The parameter estimate for C is then -1*(-6 + 12) = -1*6 = -6.

In standard least squares regression you can ask for the expanded parameter estimates to see all of them (in other words, JMP will do the math for you). Alas, that is not an option for the generalized linear model.

Dan Obermiller

1 REPLY

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Aug 28, 2014 11:36 AM
(3397 views)

This is typical for any regression when you have a categorical input variable. The parameter estimates for each level of a categorical input are "offsets" from the overall mean. This leads to a restriction that the sum of the parameter estimates for each level is zero. Because of this, if you have k-levels of a categorical factor you only need k-1 parameter estimates. The estimate for the last level is -1 times the sum of the other parameter estimates. For example, suppose your estimates for habitat type are -6 and 12 for levels A and B respectively. The parameter estimate for C is then -1*(-6 + 12) = -1*6 = -6.

In standard least squares regression you can ask for the expanded parameter estimates to see all of them (in other words, JMP will do the math for you). Alas, that is not an option for the generalized linear model.

Dan Obermiller