nl.hetroLS: CLSME estimate.

In nlr: Nonlinear Regression Modelling using Robust Methods

Description

Classic Least square based Multi Stage Estimate (CLSME) for heteroscedastic error case.

Usage

nl.hetroLS(formula, data, start = getInitial(formula, data), 
control = nlr.control(tolerance = 0.001, minlanda = 1/2^10, 
maxiter = 25 * length(start)), varmodel, tau = getInitial(varmodel, vdata), ...)

Arguments

formula	nl.form object of the nonlinear function model.
data	list of data include responce and predictor.
start	list of parameter values of nonlinear model function (θ. in f(x,θ)).
control	list of nlr.control for controling convergence criterions.
varmodel	nl.fomr object of variance function model for heteroscedastic variance.
tau	list of initial values for variance model function varmodel argument.
...	extra arguments to nonlinear regression model, heteroscedastic variance function, robust loss function or its tuning constants.

Details

Least square based estimate for nonlinear regression with hetroscedastic error when variance is a general function of unkown parameters.

Value

generalized fitt object nl.fitt.gn. The hetro slot include parameter estimate and other information of fitt for heteroscedastic variance model.

(parameters	nonlinear regression parameter estimate of θ.
correlation	of fited model.
form	nl.form object of called nonlinear regression model.
response	computed response.
predictor	computed (right side of formula) at estimated parameter with gradient and hessian attributes.
curvature	list of curvatures, see curvature function.
history	matrix of convergence history, collumns include: convergence index, parameters, minimized objective function, convergence criterion values, or other values. These values will be used in plot function in ploting history.
method	fittmethod object of method used for fitt.
data	list of called data.
sourcefnc	Object of class "callorNULL" source function called for fitt.
Fault	Fault object of error, if no error Fault number = 0 will return back.
vm	covariance matrix, diagonal of variance model predicted values.
rm	cholesky decomposition of vm.
gresponse	transformed of response by rm, include gradinet and hessian attributes.
gpredictor	transformed of predictor by rm, include gradinet and hessian attributes.
hetro	nl.fitt object of fited variance odel: parametersestimate of variance parameter τ formnl.form object of called varmodel. predictorvariance model computed at estimated parameter, H(x;\hatτ) responsesample variance computed used as response variable. historymatrix of convergence history, collumns include: convergence index, parameters, minimized objective function, convergence criterion values, or other values. methodfittmethod object of method used for fitt. dataresponse (z_i) and predictor t variable values, used to computing the variance model. sourcefncObject of class "callorNULL" source function called for fitt. FaultFault object of error, if no error Fault number = 0 will return back.

Note

Heteroscedastic variance can have several cases, this function assume variance is parameteric function of predictor (H(t;τ)). If data does not include the predictor variable of varmodel (t), the predicted of function model f(x;\hat θ) will replace for (t), otherwise user have to defin (t) or (x) as predictor variable of (H).

Author(s)

Hossein Riazoshams, May 2016, ongoing book. Email: [e-mail] URL http://www.riazoshams.com/nlr/

References

Riazoshams, H. (2012), Robustifying the Least Squares estimate of parameters of variance model function in nonlinear regression with heteroscedastic variance, Poster Presentation, Royal Statistical Society Conference (RSS) 2012, Telford, UK.

See Also

fittmethod, nl.form, nl.fitt, nl.fitt.gn

Examples

chkft<- nl.hetroLS(formula=nlrobj1[[14]], data=list(xr=Weights$Date,yr=Weights$Weight),
start=list(p1=2200,p2=38,p3=.11), tau=list(sg=.09,landa=2),varmodel=nlrobjvarmdls1[[1]])
chkft$parameters

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