nlr (version 0.1-3)

nl.fitt.rob-class: Class "nl.fitt.rob"

Description

Object of robust estimates of nonlinear regression model.

Objects from the Class

Objects can be created by calls of the form new("nl.fitt.rob", ...).

Slots

htheta:

Object of class "vectororNULL" optimized objective loss function is equal sum of rho function, with gradient and hessian as attribute.

rho:

Object of class "vectororNULL" computed robust \(\rho\) function, including gradient and hessian as attribute.

ri:

Object of class "vectororNULL" residuals equal predictor values minus predicted values, with gradient and hessian as attribute.

curvrob:

Object of class "listorNULL" robust Object of class "listorNULL" of PE and IE curvatures. Is not operational at the moment.

robform:

Object of class "nl.formorNULL", robust \(\rho\) function of object type "nl.form".

Nonlinear model estimates, inherited slots from nl.form object follows.

parameters:

Object of class "list", estimate of nonlinear model \(\theta\).

scale:

Object of class "numericorNULL", standard deviation scale estimate \(\sigma\).

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 \(\eta(\hat{\theta)}\).

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.

Extends

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

Methods

dlev

signature(nlfited = "nl.fitt.rob"): DLEV Difference in LEverage measure.

JacobianLeverage

signature(nlfited = "nl.fitt.rob"): Jacobian-Leverage for nonlinear regression. Usage JacobianLeverage(nlfited = "nl.fitt.rob")

parInfer

signature(object = "nl.fitt"): parameter inference function, calculate covariance matrix of parameters and their confidence interval. Usage: parInfer(object,confidence = .95)

plot

signature(x = "nl.fitt", y = "missing",control=nlr.control(history=F,length.out=NULL,singlePlot=F),...): generic function extended to nl.fitt object. Plot the object. Usage. plot(x,y="missing",control=nlr.control(). If history is TRUE the convergence of fitt will be ploted.length.out is length of incremented p[redictor to acheive smooter curve. singlePlot=F plot the model and residuals in two collumn. If the estimate be Least MEdian Square, the plotlms function is used to plot the object.

predictionI

signature(nlfited = "nl.fitt.gn"): prediction interval. Usage: predictionI(nlfited,confidence=.95,data=NULL), data is new data that will be predicting the values for them.

recalc

signature(object = "nl.fitt.rob"): recalculate the original call of the fited model by some extra options. It is created for usage in atyp function.

References

Riazoshams H, Midi H, and Ghilagaber G, 2018,. Robust Nonlinear Regression, with Application using R, Joh Wiley and Sons..

See Also

nl.fitt, nl.fitt.gn, fittmethod, nl.fitt.rgn, Fault, nl.form

Examples

# NOT RUN {
showClass("nl.fitt.rob")
# }