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

In nlr: Nonlinear Regression Modelling using Robust Methods

Description

Object for a fitted nonlinear regression model.

Objects from the Class

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

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.

Extends

Class "nl.fittorNULL", directly.

Methods

$

signature(x = "nl.fitt"): access the slot values. Usage: objectname$slotname

atypicals

signature(nlfited = "nl.fitt"): detect atypical points by calculating outlier detection measures. Usage: atypicals(nlfited)

hat

signature(x = "nl.fitt"): generalized Hat matrix from linear regression to nonlinear regression using gradient. Usage: hat(x="nl.fitt")

JacobianLeverage

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

parInfer

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

plot

signature(x = "nl.fitt", y = "missing",control=nlr.control(),...): generic function extended to nl.fitt object. Plot the object. Usage. plot(x,y="missing",control=nlr.control(history=F,length.out=NULL,singlePlot=F)). 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.

predict

signature(object = "nl.fitt"): generic function, predict nonlinear function model at estimated parameter values. Usage: predict(object,...), dots argument can include the newdata which might be new list of new values for predictor variables, if not given the original data that used for fitt will be used to calculate prediction values.

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"): generic function, recalculate the object with new arguments given in dots argument.

residuals

signature(object = "nl.fitt"): residuals of fitt. Usage: residuals((object,...)), dots argument can include data list of predictor and response variables, if data is not given the residuals will calculate for original data used in estimation.

acf

The function acf computes (and by default plots) estimates of the autocovariance or autocorrelation function of residuals. For argument details see stats acf general function.

Note

All information of a nonlinear fited model are saved in nl.fitt, thus it can be large variable of informations. The generalized form nl.fitt.gn and robust forms nl.fitt.rob and nl.fitt.rgn of a fitt is children of this object. Typically it used to save Least-Square estimation method. But it is extensively used to save other fited objects such as hetroscedastic variance parameter fitts.

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.

See Also

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

Examples

showClass("nl.fitt")

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