gdf {dglars} | R Documentation |
gdf
returns to estimate of the generalized degrees-of-freedom.
gdf(object)
object |
fitted |
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 gdf
function
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 luigi.augugliaro@unipa.it
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.
dglars
, AIC.dglars
, BIC.dglars
and summary.dglars
.
set.seed(123) n <- 100 p <- 10 X <- matrix(rnorm(n*p), n, p) b <- 1:2 eta <- b[1] + X[,1] * b[2] mu <- binomial()$linkinv(eta) y <- rbinom(n, 1, mu) fit <- dglars(y ~ X, binomial) gdf(fit)