nlr (version 0.1-3)

nl.MLE: Nonlinear MLE

Description

MLE estimate of a nonlinear function. with hetro variance model function, and weights.

Usage

nl.MLE(formula, data, start = getInitial(formula, data), vm = NULL, 
rm = solve(t(chol(vm))), 
control =nlr.control(derivfree = T), 
varmodel = NULL, tau = varmodel$par, ...)

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 (\(\theta\).

vm

optional covariance matrix.

rm

optional cholesky decomposition of covariance matrix.

control

list of nlr.control for controling convergence criterions. Defaul value of derivfree is "True", force function to use derivative free methods. But it can be "False" to use derivative based, has faster convergence.

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.

Value

Depending given options different fitt object will result as follow

• if vm=NULL and varmodel=NULLrepresent homogeneous and uncorrelated erro, output is nl.fitt object generated by nlsqr or nlsnm for derivative based and derivative free method respectivly given by derivfree option.

• if vm=NULL and varmodel is given represent heteroscedastic variance case, output is nl.fitt.gn generated by nl.robhetroWM, depends on using derivative free method or no.

• if vm is given represent general covariance matrix as weight, ouput is nl.fitt.gn generated by nlsqr.gn.

Details

Calculate Maximum Likelihood estimate in several sitautions, if varmodel is given the hetroscedastic variance consider. If vm or rm is given, weighted is computing.

References

Riazoshams, H,. 2010. Outlier detection and robust estimation methods for nonlinear regression having autocorrelated and heteroscedastic errors. PhD thesis disertation, University Putra Malaysia.

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

See Also

nlsqr.gn, nl.robhetroWM,nl.fitt, nl.fitt.gn, nlsnm, nlsqr, nlr.control

Examples

# NOT RUN {
## The function is currently defined as
"nl.MLE"
# }