Skip to main content

View full-text article in PMC

. Author manuscript; available in PMC: 2022 Oct 1.

Published in final edited form as: Neurosci Biobehav Rev. 2021 Jul 26;129:282–295. doi: 10.1016/j.neubiorev.2021.07.025

Table 2.

Fit statistics for LCGA models

Unconditional models
	Number of classes	AIC	BIC	Entropy	LMR		BLRT
	Number of classes	AIC	BIC	Entropy	Value	p. value	Value	p. value
FLP	1	2125.456	2151.921	--	--	--	--	--
FLP	2	2082.808	2124.396	0.513	48.549	0.4747	50.648	<.001
FLP	3	2062.225	2118.936	0.593	27.398	0.115	28.583	<.001
FLP	4	2060.148	2131.982	0.685	9.659	0.0413	10.077	0.0984
DCHD	1	2438.852	2471.246	--	--	--	--	--
DCHD	2	2354.852	2401.237	0.535	87.16	0.0022	91.262	<.001
DCHD	3	2324.121	2383.759	0.661	36.99	0.3526	38.731	<.001
DCHD	4	2313.218	2386.109	0.598	18.054	0.1515	18.903	<.001

Conditional models
	Number of classes	AIC	BIC	Entropy	LMR		BLRT
	Number of classes	AIC	BIC	Entropy	Value	p. value	Value	p. value
FLP	2	2073.162	2122.312	0.437	62.493	0.0818	64.294	<.001
FLP	3	2053.306	2125.140	.614	30.963	0.3349	31.856	<.001
FLP	4^*	2049.907	2144.425	.664	14.968	0.1495	15.399	0.3636
DCHD	2	2349.042	2402.053	0.558	97.995	0.0099	101.069	<.001
DCHD	3	2317.012	2389.902	0.668	42.691	0.2548	44.03	<.001
DCHD	4^*	2308.839	2401.607	0.612	19.56	0.3345	--	--

Note.

^*

indicates that model parameters needed to be fixed in order to ensure convergence