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

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

  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)
  1. LPA: Latent Profile Analysis; LC: Latent Class; Log-L (H0): Log-likelihood of hypothesized model (H0)
  2. 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)