. 2019 Aug 7;6(8):190097. doi: 10.1098/rsos.190097

Table 2.

GLMM testing differences between conditions on proportional target looking over time including time, its linear, quadratic and cubic term. res = lmer(PTL_corr.mean condition × age × (poly1 + poly2 + poly3) + object + label + z.TestAge + (1 + (poly1 + poly2 + poly3) | id), $data = d_aggr$ , REML = F, control = contr.

group	factor	estimates	s.e.	lower CI	upper CI	LRT	p
overall	intercept	−0.03	0.05	−0.13	0.06	^a	^a
	object	0.03	0.00	0.02	0.03	65.29	<0.001
	label	0.01	0.00	0.00	0.02	7.98	<0.001
	z.TestAge	−0.03	0.11	−0.24	0.18	0.10	0.76
	condition:age:poly1	^a	^a	^a	^a	8.68	0.03
	condition:age:poly2	^a	^a	^a	^a	9.96	0.02
	condition:age:poly3	^a	^a	^a	^a	3.09	0.38
18	intercept	−0.09	0.13	−0.35	0.18	^a	^a
	object	0.04	0.01	0.02	0.05	30.72	<0.001
	label	0.00	0.01	−0.01	0.01	0.01	0.94
	z.TestAge	−0.22	0.39	−1.02	0.53	0.32	0.57
	condition:poly1	0.07	0.21	−0.38	0.50	0.09	0.76
	condition:poly2	0.13	0.13	−0.10	0.37	1.02	0.31
	condition:poly3	−0.00	0.10	−0.21	0.19	0.00	0.97
30	intercept	−0.02	0.10	−0.22	0.19	^a	^a
	object	−0.01	0.01	−0.02	0.00	1.76	0.18
	label	−0.01	0.01	−0.02	0.00	2.59	0.11
	z.TestAge	0.12	0.23	−0.34	0.60	0.28	0.60
	condition:poly1	−0.07	0.19	−0.44	0.29	0.15	0.70
	condition:poly2	−0.27	0.12	−0.48	−0.05	4.99	0.02
	condition:poly3	−0.09	0.08	−0.24	0.07	1.16	0.28
3–4	intercept	0.08	0.13	−0.18	0.36	^a	^a
	object	0.04	0.01	0.03	0.06	36.99	<0.001
	label	0.05	0.01	0.04	0.06	48.70	<0.001
	z.TestAge	−0.07	0.10	−0.28	0.12	0.48	0.49
	condition:poly1	0.50	0.21	0.06	0.87	5.36	0.02
	condition:poly2	0.23	0.14	−0.02	0.53	2.67	0.10
	condition:poly3	−0.17	0.09	−0.35	−0.01	3.77	0.05
adults	intercept	0.64	2.15	−3.77	4.90	^a	^a
	object	0.03	0.01	0.02	0.04	42.12	<0.001
	label	−0.01	0.01	−0.02	0.00	2.07	0.15
	z.TestAge	0.30	1.52	−2.81	3.31	0.04	0.85
	condition:poly1	−0.28	0.17	−0.61	0.05	2.80	0.09
	condition:poly2	0.09	0.10	−0.11	0.29	0.73	0.39
	condition:poly3	0.01	0.07	−0.13	0.16	0.02	0.88

^aNote that coefficients of interactions can only be interpreted in relation to the respective baseline levels of the interacting variables. Furthermore, the significance level of intercepts can only be interpreted in a meaningful way when effects on the intercept are tested. Thus, these values are not displayed here because of limited informativity.