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tips:models_with_or_without_intercept

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tips:models_with_or_without_intercept [2019/06/30 11:43] – external edit 127.0.0.1tips:models_with_or_without_intercept [2020/03/19 14:25] – [Models with Continuous Moderators] Wolfgang Viechtbauer
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 H_0: \mu_a = \mu_r = \mu_s = 0. H_0: \mu_a = \mu_r = \mu_s = 0.
 $$ $$
-This test is clearly significant ($p = .0011), which indicates that we can reject the null hypothesis that the (average) log risk ratio is zero for all three methods of allocation.+This test is clearly significant ($p = .0011$), which indicates that we can reject the null hypothesis that the (average) log risk ratio is zero for all three methods of allocation.
  
 Again, we could use the ''anova()'' function to carry out explicitly the same test with: Again, we could use the ''anova()'' function to carry out explicitly the same test with:
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 </code> </code>
  
-When the model only includes continuous (i.e., numeric) predictors/moderators, then removing the intercept does just that: it removes the intercept. Hence, the model above forces the intercept to be 0, that is, it assumes that at the equator, the (average) log risk ratio is exactly zero. This seems like a rather strong assumption to make, so I would not recommend doing so. In fact, only in rare cases is it ever appropriate to remove the intercept term from a regression model.+When the model only includes continuous (i.e., numeric) predictors/moderators, then removing the intercept does just that: it removes the intercept. Hence, the model above forces the intercept to be 0, that is, it assumes that at the equator, the (average) log risk ratio is exactly zero. This seems like a rather strong assumption to make, so I would not recommend doing so. In fact, only in rare cases is it ever appropriate to remove the intercept term from a regression model (that involves only continuous predictors/moderators).
  
 ==== References ==== ==== References ====
tips/models_with_or_without_intercept.txt · Last modified: 2022/08/03 11:34 by Wolfgang Viechtbauer