if I set model as 'linear' in 'anovan', is 'anovan' equivalent to applying respectively 'anova1' to the factors?

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Hi, I have the data consisting of the 6 factors with the 3 levels of each factor.
If I set model as 'linear' in 'anovan', is the result from applying 'anova1' to those factors respectively equivalent to the result from applying 'anovan' to the data?
The result means 'F-Value' and 'p-Value' in this case.

Answers (1)

Jeff Miller
Jeff Miller on 17 Sep 2022
no, those are not equivalent, as you should be able to verify by applying them both to the same dataset. With its default 'linear' model, anovan fits a model that has main effects for all 6 of the factors (among other things, this removes the effects of all factors from error, leaving a common error term that is used for testing all factors). In constrast, anova1 only considers a single factor in its model, so the effects of all of the other 5 unconsidered factors go into the error term. If you did anova1 on each single factor, they would (almost certainly) all have different numerical values for their error terms.

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