Table 2.

Goodness of fit statistics for 1 to 4 class models

Class	AIC	BIC	aBIC	Entropy	LMR(p)	BLRT(p)	Sample proportion (%) per class
1	135405.166	135553.921	135471.315	n.a.	n.a.	n.a.	n.a.
2	125201.556	125430.410	125303.324	0.935	< 0.001	< 0.001	71.37/28.63
3	122468.063	122777.016	122605.449	0.908	< 0.001	< 0.001	55.73/32.17/12.10
4	121328.755	121717.807	121501.759	0.916	0.3132	< 0.001	25.67/55.70/8.23/10.40

Note: AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; aBIC, adjusted Bayesian Information Criterion; LMR, Lo–Mendell–Rubin; BLRT, bootstrap likelihood ratio test; n.a., not applicable. An entropy value of more than 0.80 indicates good discrimination. Significant results from LMR and BLRT tests indicate that the more complex model (k-class) fit the data better. Lower AIC, BIC, and aBIC values indicate better model fit