R: Summarizing local distance-based generalized linear model...

summary.ldbglm {dbstats}

R Documentation

Summarizing local distance-based generalized linear model fits

Description

summary method for class "ldbglm".

Usage

  ## S3 method for class 'ldbglm'
summary(object,dispersion = NULL,...)

Arguments

`object`	an object of class `ldbglm`. Result of `ldbglm`.
`dispersion`	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.

Value

A list of class summary.ldgblm containing the following components:

`nobs`	number of observations.
`trace.hat`	Trace of smoother matrix.
`call`	the matched call.
`family`	the `family` object used.
`deviance`	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.
`df.residual`	the residual degrees of freedom.
`null.deviance`	the deviance for the null model.
`df.null`	the residual degrees of freedom for the null model.
`iter`	number of Fisher Scoring (`dblm`) iterations.
`deviance.resid`	the deviance residuals for each observation: sign(y-mu)*sqrt(di).
`pears.resid`	the raw residual scaled by the estimated standard deviation of `y`.
`dispersion`	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.
`kind.kernel`	smoothing kernel function.
`method.h`	method used to decide the optimal bandwidth.
`h.opt`	the optimal bandwidth h used in the fitting proces (`if method.h!=user.h`).
`crit.value`	value of criterion defined in `method.h`.

Author(s)

Boj, Eva <evaboj@ub.edu>, Caballe, Adria <adria.caballe@upc.edu>, Delicado, Pedro <pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>

References

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.