loss.SSQ: Sum of squared loss function.

In nlr: Nonlinear Regression Modelling using Robust Methods

Description

This function used in nlsnm function to compute the least square estimate using derivative free Nelder-Mead algorithm.

Usage

loss.SSQ(formula, data, start, vm = NULL, rm = NULL, ...)

Arguments

formula	nl.form object of nonlinear regression model.
data	list of data include responce and predictor.
start	list of parameter values of nonlinear model function (θ in f(x,θ)), initial values or increament during optimization procedure.
vm	optional covariance matrix.
rm	optional cholesky decomposition of covariance matrix.
...	any other arguments might be used in formula, robfunc or tuning constants in rho function.

Details

loss.SSQ compute the sum of square of residuals, it is optimized to be used in nlsnm function, since optimization method Nelder-Mead is derivative free the result does not include derivatives.

Value

result <- list(value = value,correlation=correlation,fmod=fmod)

list values:

value	sum of squared residuals.
correlation	correlation of model
fmod	computed function (transformed by R) contains esponse and or its gradient and hessian predictor and or its gradient & hessian, transformed also by R.

Note

If required to compute square loss function include can use nl.robfuncs[7], see nl.robfuncs. This is implemented for internal use, might not be called directly by user.

Author(s)

Hossein Riazoshams, May 2014. Email: [e-mail] URL http://www.riazoshams.com/nlr/

References

Robust Nonlinear Regression, Theories and Methods with Practical Guides for R Packages. Riazoshams et al.

See Also

nlsnm, nl.robfuncs

Examples

## The function is currently defined as
"loss.SSQ"

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