Table 1.

Results of 1000 permutations of the multi-model inference procedure performed on four variables of dyadic relationship strength as predictors of the number of coalitions formed during biennial periods. The table contains the model diagnostics and estimates of predictors for the top three GLMM models, which fell within the top 95% cumulative weight confidence set on more than 95% of permutations, the null model and the weighted average estimates and error based on these top models. Bold values indicate where the confidence intervals around effect size estimates excluded zero in more than 95% of permutations. All continuous measures are z-scored.

model	1	2	3	null	avg.
AICC mean	683.055	684.877	685.67	727.442
d.f.	9	10	8	6
ΔAICC	0	1.822	2.615	44.388
weight	0.474	0.192	0.139	0
cumulative weight	0.474	0.666	0.805	1
freq. in 95% set	1000	1000	979	0
conditional R2	0.093	0.093	0.094	0.101
predictors
party association
β	0.315	0.324	0.453	—	0.340
s.e.(β)	0.119	0.121	0.102	—	0.127
% CI exclude 0	96.4	97	100	—	98.4
five-metre association
β	0.231	0.22	—	—	0.190
s.e.(β)	0.105	0.108	—	—	0.27
% CI exclude 0	86.4	61.1	—	—	8.2
grooming presence [Y]
β	0.532	0.515	0.667	—	0.550
s.e.(β)	0.193	0.196	0.184	—	0.199
% CI exclude 0	99.6	98.4	100.0	—	99.5
grooming duration
β	—	0.049	—	—	0.012
s.e.(β)	—	0.105	—	—	0.056
% CI exclude 0	—	0	—	—	0
control variables
intercept
β	−10.385	−10.382	−10.436	−10.088	−10.39
s.e.(β)	0.327	0.328	0.324	0.357	0.327
% CI exclude 0	100.0	100.0	100.0	100.0	100
summed dyad rank
β	−0.403	−0.415	−0.366	−0.467	−0.400
s.e.(β)	0.132	0.134	0.133	0.146	0.133
% CI exclude 0	99.8	100.0	98.8	100.0	99.8
kinship
β	−0.853	−1.028	−0.558	1.205	−0.847
s.e.(β)	0.530	0.644	0.518	0.413	0.576
% CI exclude 0	11.9	12.4	0.3	99.6	4.6