Latent profile analysis in frontotemporal lobar degeneration and related disorders: clinical presentation and SPECT functional correlates

Table 3 Goodness of fit statistics, and class frequencies for LPA models from 1 to 5 latent classes.

	LC (1)	LC (2)	LC (3)	LC (4)	LC (5)
Log-L (H0)	-29663.70	-2636.05	-2554.08	-2512.92	-2507.12
n parameters	32	49	66	83	100
AIC	5991.4	5370.1	5240.2	5191.8	5214.2
BIC	6073.8	5493.7	5406.6	5401.1	5466.4
Entropy		0.92	0.951	0.963	0.945
Class frequencies (%)
n1	92 (100)	44 (47.8)	17 (18.5)	8 (8.7)	28 (30.4)
n2		48 (52.2)	38 (41.3)	12 (13.0)	3 (3.3)
n3			37 (40.2)	33 (35.9)	17 (18.5)
n4				39 (42.4)	29 (31.5)
n5					15 (16.3)

LPA: Latent Profile Analysis; LC: Latent Class; Log-L (H0): Log-likelihood of hypothesized model (H0)
AIC: Akaike's Information Criterion (= -2 × model log-likelihood + 2 × number of model parameters); BIC: Bayesian Information Criterion (= -2 × model log-likelihood + log(n) × number of model parameters)

ISSN: 1471-2377