logLik {saemix}

Extract likelihood from an SaemixObject resulting from a call to saemix

Description

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.

Usage

## S3 method for class 'SaemixObject'
logLik(object, method = c("is", "lin", "gq"), ...)

## S3 method for class 'SaemixObject'
AIC(object, method = c("is", "lin", "gq"), ..., k = 2)

## S3 method for class 'SaemixObject'
BIC(object, method = c("is", "lin", "gq"), ...)

## S3 method for class 'covariate'
BIC(object, method = c("is", "lin", "gq"), ...)

Arguments

object

name of an SaemixObject object

method

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

k

numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC

Details

BIC.covariate implements the computation of the BIC from Delattre et al. 2014.

Value

Returns the selected statistical criterion (log-likelihood, AIC, BIC) extracted from the SaemixObject, computed with the 'method' argument if given (defaults to IS).

Author(s)

Emmanuelle Comets [e-mail]

Audrey Lavenu

Marc Lavielle

Maud Delattre

References

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.

See Also

AIC,BIC, saemixControl, saemix