nl.fitt.gn: Class '"nl.fitt.gn"'

In nlr: Nonlinear Regression Modelling using Robust Methods

Description

Object for Generalized fitt of nonlinear regression, inherited from nl.fitt object. Classic estimates of Heterogeneouity, Autocorrelated, or Weighted fitt will be saved in this object.

Objects from the Class

Objects can be created by calls of the form new("nl.fitt.gn",...). Include all slots of nl.fitt and another slots for saving heterosedastic variance fit or autocorrelated error parameters.

Slots

parameters:

Object of class "list", estimate of nonlinear model θ.

scale:

Object of class "numericorNULL", standard deviation scale estimate σ.

correlation:

Object of class "numericorNULL", correlation structure of error.

form:

Object of class "nl.form" of nonlinear model.

response:

Object of class "vectororMatrix" response, left side of formula.

predictor:

Object of class "vectororMatrix", estimated predictor η(\hat{θ)}.

curvature:

Object of class "listorNULL" of PE and IE curvatures.

history:

Object of class "matrixororNULL" convergence computations in iteration procedures, include parameters, objective function and other parameters depends on the method.

method:

Object of class "fittmethodorNULL" method of iteration used, contains main method, functions and sub methods. See fittmethod.

data:

Object of class "list" data used in computation, including response and predictor variables.

sourcefnc:

Object of class "callorNULL" source function called for fitt.

Fault:

Object of class "Fault" of error or warnings if happened.

others:

Object of class "listorNULL" of other computations, as an example the object of outlier detection measures will be saved in this slot later on.

So far was parent slot nl.fitt. Following slots are other that represent generalized nonlinear regression fitt.

vm:

Object of class "matrix" of variance covariance matrix of error.

rm:

Object of class "matrix" of correlated error.

hetro:

Object of class "nl.fittorNULL", include object nl.fitt of heteroscedastic error fit, or NULL for non hetroscedastic. It include parameter estimates of hetroscedastic variance τ and all other slots of nl.fitt object which represent the variance function fitt information.

autcorr:

Object of class "listorNULL" of autocorrelated error.

autpar:

Object of class "listorNULL" of aprameters for autocorrelated error.

gresponse:

Object of class "vectororMatrix" generalized response, transformed response equal R \times y, for cholesky decomposition R of covariance matrix of error.

gpredictor:

Object of class "vectororMatrix" generalized predictor, transformed of predictor equal R \times η(θ), for cholesky decomposition R of covariance matrix of error.

Extends

Class "nl.fitt", directly. Class "nl.fittorNULL", by class "nl.fitt", distance 2.

Methods

atypicals

signature(nlfited = "nl.fitt.gn"): Compute statistical measures to identify outliers.

parInfer

signature(object = "nl.fitt.gn"): parameter inference, covariance matrix of parameters.

predictionI

signature(nlfited = "nl.fitt.gn"): prediction interval.

recalc

signature(object = "nl.fitt"): generic function, recalculate the object with new arguments given in dots argument.

residuals

signature(object = "nl.fitt.gn"): residuals of fitt.

Note

nl.fitt.gn inherit nl.fitt object. It include heterogeneous or autocorrelated fitt. The heteroscedastic fitt result stores in hetro slot, and autocorelation result stores in correlation,autcor slot. meanwhile the vm, rm include contains general of covariance and correlation matrix of both heteroscedastic and autocorrelated informations. See Seber and Wild (2003) for details in generalized Nonlinear model.

Author(s)

Hossein Riazoshams, May 2014. Email: [e-mail] URL http://www.riazoshams.com/nlr/

References

Riazoshams H, Midi H, and Ghilagaber G, 2018,. Robust Nonlinear Regression, with Application using R, Joh Wiley and Sons. Seber, G., A. F. and Wild, C. J. (2003). Nonlinear Regression. New York: John Wiley & Sons, Inc.

See Also

nl.fitt, fittmethod.

Examples

showClass("nl.fitt.gn")

nlr documentation built on July 31, 2019, 5:09 p.m.