summarytable: Summary of models

Description Usage Arguments Details Value Author(s) See Also

View source: R/summarytable.R

Description

This function provides a table summarizing the results of different models fitted by hlme, lcmm, multlcmm or Jointlcmm.

Usage

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summarytable(
  m1,
  ...,
  which = c("G", "loglik", "npm", "BIC", "%class"),
  display = TRUE
)

Arguments

m1

an object of class hlme, lcmm, multlcmm or Jointlcmm

...

further arguments, in particular other objects of class hlme, lcmm, multlcmm or Jointlcmm

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)

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 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 as BIC -2*sum[c_ig*log(pi_ig)] where c_ig is the posterior class membership -

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


lcmm documentation built on Jan. 31, 2022, 9:06 a.m.