Description Usage Arguments Details Value Author(s) See Also
This function provides a table summarizing the results of different models
fitted by hlme
, lcmm
, multlcmm
or Jointlcmm
.
1 2 3 4 5 6  summarytable(
m1,
...,
which = c("G", "loglik", "npm", "BIC", "%class"),
display = TRUE
)

m1 
an object of class 
... 
further arguments, in particular other objects of class

which 
character vector indicating which results should be returned. Possible values are "G", "loglik", "conv", "npm", "AIC", "BIC", "SABIC", "entropy", "ICL", "%class". 
display 
logical indicating whether the table should be printed (the default) or not (display=FALSE) 
Can be reported the usual criteria used to assess the fit and the clustering of the data:  maximum loglikelihood L (the higher the better)  number of parameters P, number of classes G, convergence criterion (1 = converged)  AIC (the lower the better) computed as 2L+2P  BIC (the lower the better) computed as 2L+ P log(N) where N is the number of subjects  SABIC (the lower the better) computed as 2L+ P log((N+2)/24)  Entropy (the closer to one the better) computed as 1sum[pi_ig*log(pi_ig)]/(N*log(G)) where pi_ig is the posterior probability that subject i belongs to class g  ICL (the lower the better) computed as BIC 2*sum[c_ig*log(pi_ig)] where c_ig is the posterior class membership 
a matrix giving for each model the values of the requested indexes. By default, the number a latent classes, the loglikelihood, the number of parameters, the BIC and the posterior probability of the latent classes.
Cecile ProustLima, Viviane Philipps
summary
, hlme
, lcmm
,
multlcmm
, Jointlcmm
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