gdf is used to compute the first order estimator of the generalized degrees-of-freedom for logistic regression model.
For a general nonlinear modelling procedure, a more rigorous definition of degrees-of-freedom is obtained using the covariance penalty theory (Efron, 2004).
When we work with a logistic regression model defined in a low dimensional setting, the
gdf function can be used to compute the first order estimator
proposed in Augugliaro et al. (2013). How to define an estimator of the generalized degrees-of-freedom in a high-dimensional setting is still an open
question. Simulation studies seem to show that for a Poisson regression model the number of nonzero coefficients can be considered a satisfying approximation
to the generalized degrees-of-freedom, for this reason the first order estimator is not implemented for this model.
gdf returns a vector of length
np with the generalized degrees-of-freedom.
Maintainer: Luigi Augugliaro firstname.lastname@example.org
Augugliaro L., Mineo A.M. and Wit E.C. (2014) dglars: An R Package to Estimate Sparse Generalized Linear Models, Journal of Statistical Software, Vol 59(8), 1-40. http://www.jstatsoft.org/v59/i08/.
Augugliaro L., Mineo A.M. and Wit E.C. (2013) dgLARS: a differential geometric approach to sparse generalized linear models, Journal of the Royal Statistical Society. Series B., Vol 75(3), 471-498.
Efron B. (2004) The estimation of prediction error: covariance penalties and cross-validation, Journal of the American Statistical Association, Vol. 99(467), 619-632.
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