This function provides a table summarizing the results of different models
fitted by hlme, lcmm, multlcmm, Jointlcmm,
mpjlcmm or externVar.
summarytable(
m1,
...,
which = c("G", "loglik", "npm", "BIC", "%class"),
display = TRUE
)a matrix giving for each model the values of the requested indexes. By default, the number a latent classes, the log-likelihood, the number of parameters, the BIC and the posterior probability of the latent classes.
an object of class hlme, lcmm, multlcmm,
Jointlcmm, mpjlcmm, externVar or externVar.
further arguments, in particular other objects of class
hlme, lcmm, multlcmm, Jointlcmm or mpjlcmm.
character vector indicating which results should be returned. Possible values are "G", "loglik", "conv", "npm", "AIC", "BIC", "SABIC", "entropy", "ICL", "ICL1", "ICL2", "%class".
logical indicating whether the table should be printed (the default) or not (display=FALSE)
Cecile Proust-Lima, Viviane Philipps
Can be reported the usual criteria used to assess the fit and the clustering of the data: - maximum log-likelihood 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 1+sum[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 in two ways : ICL1 = BIC - sum[pi_ig*log(pi_ig)] or ICL2 = BIC - 2*sum(log(max(pi_ig)), where the max is taken over the classes for each subject. - %class computed as the proportion of each class based on c_ig
summary, hlme, lcmm,
multlcmm, Jointlcmm