summarytable {lcmm} | R Documentation |
Summary of models
Description
This function provides a table summarizing the results of different models
fitted by hlme
, lcmm
, multlcmm
, Jointlcmm
,
mpjlcmm
or externVar
.
Usage
summarytable(
m1,
...,
which = c("G", "loglik", "npm", "BIC", "%class"),
display = TRUE
)
Arguments
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", "ICL1", "ICL2", "%class". |
display |
logical indicating whether the table should be printed (the default) or not (display=FALSE) |
Details
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 in two ways : ICL1 = 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. -
Value
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.
Author(s)
Cecile Proust-Lima, Viviane Philipps
See Also
summary
, hlme
, lcmm
,
multlcmm
, Jointlcmm