Table 4.

Comparison of estimated time and conditional AIC between indiCAR and other methods when data are generated with spatial random effect parameter, $\mathit{\lambda }=0.75$

Sample per group	Total sample	Time to convergence (s)				Conditional AIC
		indiCAR	glmer with random intercept	hlmer with random intercept	hlmer with CAR	indiCAR	glmer with random intercept	hlmer with random intercept	hlmer with CAR
Data generated in 100 groups
1:50	2517	0.48	2.34	0.39	2.10	1748.78	1883.67	1881.69	1881.81
1:100	4688	3.30	3.40	0.38	6.75	2821.61	2899.41	2897.73	2897.83
1:500	26,519	4.23	23.71	1.92	15.52	15,865.50	15,943.65	15,943.58	15,943.55
1:1000	52,911	188.19	61.84	3.62	Not feasible	32,632.45	32,669.39	32,669.18	Not feasible
Data generated in 400 groups
1:50	10,118	51.55	14.81	2.65	138.33	5935.14	6323.31	6309.56	6309.44
1:100	20,652	36.74	25.53	4.20	434.66	12,476.61	12,893.85	12,889.04	12,889.13
1:500	103,267	85.75	73.45	22.31	Not feasible	60,233.22	60,533.49	60,533.24	Not feasible
1:1000	205,739	113.65	236.95	46.23	Not feasible	120,212.20	120,423.70	120,423.00	Not feasible