gdf returns to estimate of the generalized degrees-of-freedom.
For a general nonlinear modelling procedure, a rigorous definition of
degrees-of-freedom is obtained using the covariance penalty theory
(Efron, 2004). This theory was used in Augugliaro et al. (2013) to
define a measure of model complexity for the dgLARS method, called
“generalized degrees-of-freedom”. The
implements the estimator proposed in Augugliaro et al. (2013).
gdf returns a vector of length
np with the generalized degrees-of-freedom.
Luigi Augugliaro and Hassan Pazira
Maintainer: Luigi Augugliaro [email protected]
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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