nlr (version 0.1-3)

nlsqr: Least Square estimate.

Description

Least Square estimate of a nonlinear function, Using QR-decomposition of Gradient matrix.

Usage

nlsqr(formula, data, start = getInitial(formula, data), 
control = nlr.control(tolerance = 1e-04, minlanda = 1/2^10, 
maxiter = 25 * length(start)))

Arguments

formula

nl.form object of the nonlinear function model. See nl.form object.

data

list of data with the response and predictor as name of variable.

start

list of starting value parameter, name of parameters must be represented as names of variable in the list.

control

nlr.control object, include tolerance, maxiter,... see nlr.control.

Value

result is object of nl.fitt (nonlinear fitt robust) for homogeneous and uncorrelated variance.

parameters

nonlinear regression parameter estimate of \(\theta\).

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.

Details

It is used to minimize the square loss function, using QR-decomposition of gradient matrix, thus the nonlinear function model formula must return back Gradient.

References

Bates, D. M., and Watts, D. G. (1988). Nonlinear regression analysis and its applications. New York: John Wiley & Sons.

See Also

nl.form, nlsnm, nlr.control, nl.fitt, curvature, Fault

Examples

# NOT RUN {
## The function is currently defined as
"nlsqr"
# }