The likelihood in saemix can be computed by one of three methods: linearisation (linearisation of the model), importance sampling (stochastic integration) and gaussian quadrature (numerical integration). The linearised likelihood is obtained as a byproduct of the computation of the Fisher Information Matrix (argument FIM=TRUE in the options given to the saemix function). If no method argument is given, this function will attempt to extract the likelihood computed by importance sampling (method="is"), unless the object contains the likelihood computed by linearisation, in which case the function will extract this component instead. If the requested likelihood is not present in the object, it will be computed and aded to the object before returning.
# S3 method for SaemixObject
logLik(object, method = c("is", "lin", "gq"), ...)# S3 method for SaemixObject
AIC(object, method = c("is", "lin", "gq"), ..., k = 2)
# S3 method for SaemixObject
BIC(object, method = c("is", "lin", "gq"), ...)
# S3 method for covariate
BIC(object, method = c("is", "lin", "gq"), ...)
Returns the selected statistical criterion (log-likelihood, AIC, BIC) extracted from the SaemixObject, computed with the 'method' argument if given (defaults to IS).
name of an SaemixObject object
character string, one of c("is","lin","gq"), to select one of the available approximations to the log-likelihood (is: Importance Sampling; lin: linearisation and gq: Gaussian Quadrature). See documentation for details
additional arguments
numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC
Emmanuelle Comets emmanuelle.comets@inserm.fr
Audrey Lavenu
Marc Lavielle
Maud Delattre
BIC.covariate implements the computation of the BIC from Delattre et al. 2014.
E Comets, A Lavenu, M Lavielle M (2017). Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm. Journal of Statistical Software, 80(3):1-41.
E Kuhn, M Lavielle (2005). Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis, 49(4):1020-1038.
E Comets, A Lavenu, M Lavielle (2011). SAEMIX, an R version of the SAEM algorithm. 20th meeting of the Population Approach Group in Europe, Athens, Greece, Abstr 2173.