tips:models_with_or_without_intercept
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tips:models_with_or_without_intercept [2021/02/12 15:53] – Wolfgang Viechtbauer | tips:models_with_or_without_intercept [2021/02/12 16:09] – Wolfgang Viechtbauer | ||
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Line 106: | Line 106: | ||
& | & | ||
\end{align} | \end{align} | ||
- | But what about the contrast between | + | But what about the contrast between systematic |
$$ | $$ | ||
\beta_2 - \beta_1 = (\mu_r - \mu_a) - (\mu_s - \mu_a) = \mu_r - \mu_s | \beta_2 - \beta_1 = (\mu_r - \mu_a) - (\mu_s - \mu_a) = \mu_r - \mu_s | ||
$$ | $$ | ||
- | so this difference | + | so this contrast |
<code rsplus> | <code rsplus> | ||
anova(res, L=c(0, | anova(res, L=c(0, | ||
Line 224: | Line 224: | ||
</ | </ | ||
- | It is important to realize that this does not test whether there are differences between the different forms of allocation (this is what we tested earlier in the model that included the intercept term). However, we can again use contrasts of the model coefficients to test differences between the levels. | + | It is important to realize that this does not test whether there are differences between the different forms of allocation (this is what we tested earlier in the model that included the intercept term). However, we can again use contrasts of the model coefficients to test differences between the levels. |
<code rsplus> | <code rsplus> | ||
- | anova(res, L=rbind(c(-1, | + | anova(res, L=rbind(c(-1, |
</ | </ | ||
<code output> | <code output> | ||
- | Hypotheses: | + | Hypotheses: |
- | 1: | + | 1: |
- | 2: -factor(alloc)alternate + factor(alloc)systematic = 0 | + | 2: -factor(alloc)alternate |
+ | 3: -factor(alloc)random | ||
Results: | Results: | ||
- | | + | |
- | 1: -0.4478 0.5158 -0.8682 0.3853 | + | 1: -0.4478 0.5158 -0.8682 0.3853 |
- | 2: | + | 2: |
+ | 3: | ||
+ | </ | ||
+ | These are now the exact same results we obtained earlier for the model that included the intercept term. | ||
+ | Note that the output does not contain an omnibus test for the three contrasts because the matrix with the contrast coefficients ('' | ||
+ | <code rsplus> | ||
+ | anova(res, L=rbind(c(-1, | ||
+ | </ | ||
+ | <code output> | ||
Omnibus Test of Hypotheses: | Omnibus Test of Hypotheses: | ||
QM(df = 2) = 1.7675, p-val = 0.4132 | QM(df = 2) = 1.7675, p-val = 0.4132 | ||
</ | </ | ||
- | These are now the exact same results we obtained earlier for the model that included the intercept term. | ||
==== Parameterization ==== | ==== Parameterization ==== |
tips/models_with_or_without_intercept.txt · Last modified: 2022/08/03 11:34 by Wolfgang Viechtbauer