Table 6.

Test for significance of random effects of the models for bites and zig-zags at the gravid female, N = 24 (number of fixed effects parameters = 4).

	AIC	Log(L)	Cov. P	Test	Component tested	χ2,*	df	p
1	2182.54	−1087.27	4
2	2172.70	−1081.35	5	1 vs. 2	Intercept (same variance)	11.84	1	<0.0001
3	2129.25	−1058.63	6	2 vs. 3	Slope (same variance)	45.45	1	<0.0001
4	2145.57	−1066.78	6	2 vs. 4	Intercept (different variance)	29.13	1	<0.0001
5	2133.20	−1058.60	8	4 vs. 5	Slope (different variance)	16.37	2	0.0002
6	2108.31	−1044.16	10	5 vs. 6	COVI,S within behav	28.89	2	<0.0001
7	2088.89	−1030.45	14	6 vs. 7	COVI,S between behav	27.42	4	<0.0001

AIC, Akaike Information Criterion (AIC); log(L), log likelihood; Cov P, the number of covariance parameters; df, degrees of freedom. The final model is indicated in bold.

^*Log-likelihood ratio tests.

Model 1 includes the final fixed effects structure with no random effects. Model 2 contains the same fixed effects as model 1 with random intercepts for bites and zig-zags, assumed to have the same variance. Model 3 contains the same fixed and random effects as model 2 with random slopes for bites and zig-zags, assumed to have the same variance. Model 4 contains the same fixed and random effects as model 2 with random intercepts for bites and zig-zags, allowed to have different variances. Model 5 contains the same fixed and random effects as model 4 with random slopes for bites and zig-zags, which are allowed to have different variances. Model 6 contains the same fixed and random effects as model 5 but random effects within bites and zig-zags are allowed to have nonzero covariance. Model 7 contains the same fixed and random effects as model 6, but allows for all possible covariances between random effects between behaviours (i.e., COV_I,I, COV_S,S, COV_{I bites, S zz}, COV_{I zz, S bites}).