nlr (version 0.1-3)

nlmest.RWT: Nonlinear MM-estimate using reweighting method.

Description

Compute MM-estimate using reweighting method developed by Stromberg.

Usage

nlmest.RWT(formula, data, start = getInitial(formula, data), robfunc, 
control = nlr.control(tolerance = 0.001, minlanda = 1/2^25, 
maxiter = 25 * length(start), trace = F), vm = NULL, rm = eiginv(t(chol(vm))), ...)

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. In heterogeneous case if it include response variable values of heterogenous variance function it asume variance function is function of predictor \(H(x_i,\tau)\), otherwise it assume is a function of predictor \(H(f(x_i,\theta),\tau)\).

start

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

robfunc

nl.form object of robust function used for downgrading.

vm

optional covariance matrix of residuals, used for nonlinear generalized M-estimate.

rm

optional correlation matrix, used for nonlinear generalized M-estimate. rm is correlation matrix of vm, thus only vm is enough to be given. It can be given by user also but not necessary automatically will be calculated by argument eiginv(t(chol(vm))).

…

any other argument passed to formula, robfnc, or optimization function.

control

nlr.control option variables.

Value

result is object of nl.fitt.rob (nonlinear fitt robust) for homogeneous variance, and nl.fitt.rgn for heterogeneous (not developed yet) and autocorrelated error (nonlinear fitt robust generalized), see nl.fitt.rgn object detail.

Details

Compute MM-estimate using reweighting method developed by Stromberg.

References

Stromberg, A. J. (1993). Computation of High Breakdown Nonlinear Regression Parameters, Journal of American Statistical Association 88(421): 237-244.

See Also

nlmest.NLM

Examples

# NOT RUN {
## The function is currently defined as
"nlmest.RWT"
# }