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--- resources:lme [2015/06/24 11:23] – anthony
+++ resources:lme [2019/05/22 16:08] (current) – external edit 127.0.0.1
@@ Line 51: / Line 51: @@
 https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/015590.html
-----
-In this case the coefficient corresponds to one of the
-terms in the model and I would advocate performing a likelihood ratio
-test comparing the two models
-<code rsplus>
-fm <- glmer(SameSite~BreedSuc1+Sex+(1|Bird), family="binomial")
-fm0 <- glmer(SameSite~Sex+(1|Bird), family="binomial")  # the null
-hypothesis model
-anova(fm0, fm)
-</code>
-Even though the function is called anova it will, in this case,
-perform a likelihood ratio test (LRT).  It also prints the values of
-AIC and BIC if you prefer to compare models according to one of those
-criteria but I prefer using the likelihood ratio for nested models.
-However, before doing that comparison you should ask yourself whether
-you want to compare models that have the, apparently unnecessary term
-for Sex in them.  The way I would approach the model building is first
-to reduce the model to
-<code rsplus>
-fm1 <- lmer(SameSite~BreedSuc1+(1|Bird), family="binomial")
-</code>
-You could then compare
-<code rsplus>
-anova(fm1, fm)
-</code>
-which I presume will give a large p-value for the LRT, so we prefer
-the simpler model, fm1.  After that, I would compare
-<code rsplus>
-fm2 <- lmer(SameSite ~ 1 + (1|Bird), family="binomial")
-</code>
+>In this case the coefficient corresponds to one of the
+>terms in the model and I would advocate performing a likelihood ratio
+>test comparing the two models
+>
+><code rsplus>
+>fm <- glmer(SameSite~BreedSuc1+Sex+(1|Bird), family="binomial")
+>fm0 <- glmer(SameSite~Sex+(1|Bird), family="binomial")  # the null
+>hypothesis model
+>anova(fm0, fm)
+></code>
+>Even though the function is called anova it will, in this case,
+>perform a likelihood ratio test (LRT).  It also prints the values of
+>AIC and BIC if you prefer to compare models according to one of those
+>criteria but I prefer using the likelihood ratio for nested models.
+>However, before doing that comparison you should ask yourself whether
+>you want to compare models that have the, apparently unnecessary term
+>for Sex in them.  The way I would approach the model building is first
+>to reduce the model to
+><code rsplus>
+>fm1 <- lmer(SameSite~BreedSuc1+(1|Bird), family="binomial")
+></code>
+>You could then compare
+><code rsplus>
+>anova(fm1, fm)
+></code>
+>which I presume will give a large p-value for the LRT, so we prefer
+>the simpler model, fm1.  After that, I would compare
+><code rsplus>
+>fm2 <- lmer(SameSite ~ 1 + (1|Bird), family="binomial")
+></code>