summary method for class "ldbglm".
# S3 method for ldbglm
summary(object,dispersion = NULL,...)A list of class summary.ldgblm containing the following components:
number of observations.
Trace of smoother matrix.
the matched call.
the family object used.
measure of discrepancy or goodness of fitt. Proportional to twice the difference between the maximum log likelihood achievable and that achieved by the model under investigation.
the residual degrees of freedom.
the deviance for the null model.
the residual degrees of freedom for the null model.
number of Fisher Scoring (dblm) iterations.
the deviance residuals for each observation: sign(y-mu)*sqrt(di).
the raw residual scaled by the estimated standard
deviation of y.
the dispersion is taken as 1 for the binomial and Poisson families, and otherwise estimated by the residual Chisquared statistic (calculated from cases with non-zero weights) divided by the residual degrees of freedom.
smoothing kernel function.
method used to decide the optimal bandwidth.
the optimal bandwidth h used in the fitting proces
(if method.h!=user.h).
value of criterion defined in method.h.
an object of class ldbglm.
Result of ldbglm.
the dispersion parameter for the family used.
Either a single numerical value or NULL (the default)
arguments passed to or from other methods to the low level.
Boj, Eva <evaboj@ub.edu>, Caballe, Adria <adria.caballe@upc.edu>, Delicado, Pedro <pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>
Boj E, Delicado P, Fortiana J (2010). Distance-based local linear regression for functional predictors. Computational Statistics and Data Analysis 54, 429-437.
Boj E, Grane A, Fortiana J, Claramunt MM (2007). Selection of predictors in distance-based regression. Communications in Statistics B - Simulation and Computation 36, 87-98.
Cuadras CM, Arenas C, Fortiana J (1996). Some computational aspects of a distance-based model for prediction. Communications in Statistics B - Simulation and Computation 25, 593-609.
Cuadras C, Arenas C (1990). A distance-based regression model for prediction with mixed data. Communications in Statistics A - Theory and Methods 19, 2261-2279.
Cuadras CM (1989). Distance analysis in discrimination and classification using both continuous and categorical variables. In: Y. Dodge (ed.), Statistical Data Analysis and Inference. Amsterdam, The Netherlands: North-Holland Publishing Co., pp. 459-473.
ldbglm for local distance-based generalized linear models.