nlsr.package: nlsr-package Tools for solving nonlinear least squares...

In nlsr: Functions for Nonlinear Least Squares Solutions - Updated 2022

nlsr-package Tools for solving nonlinear least squares problems The package provides some tools related to using the Nash variant of Marquardt's algorithm for nonlinear least squares. Jacobians can usually be developed by automatic or symbolic derivatives.

Description

nlsr-package

Tools for solving nonlinear least squares problems

The package provides some tools related to using the Nash variant of Marquardt's algorithm for nonlinear least squares. Jacobians can usually be developed by automatic or symbolic derivatives.

Usage

nlsr.package()

Details

This package includes methods for solving nonlinear least squares problems specified by a modeling expression and given a starting vector of named paramters. Note: You must provide an expression of the form lhs ~ rhsexpression so that the residual expression rhsexpression - lhs can be computed. The expression can be enclosed in quotes, and this seems to give fewer difficulties with R. Data variables must already be defined, either within the parent environment or else in the dot-arguments. Other symbolic elements in the modeling expression must be standard functions or else parameters that are named in the start vector.

The main functions in nlsr are:

nlfb Nash variant of the Marquardt procedure for nonlinear least squares, with bounds constraints, using a residual and optionally Jacobian described as R functions.

nlxb Nash variant of the Marquardt procedure for nonlinear least squares, with bounds constraints, using an expression to describe the residual via an R modeling expression. The Jacobian is computed via symbolic differentiation.

wrapnlsr Uses nlxb to solve nonlinear least squares then calls nls() to create an object of type nls. nlsr is an alias for wrapnlsr

model2rjfun returns a function with header function(prm), which evaluates the residuals (and if jacobian is TRUE the Jacobian matrix) of the model at prm. The residuals are defined to be the right hand side of modelformula minus the left hand side.

model2ssgrfun returns a function with header function(prm), which evaluates the sum of squared residuals (and if gradient is TRUE the gradient vector) of the model at prm.

modelexpr returns the expression used to calculate the vector of residuals (and possibly the Jacobian) used in the previous functions.

Author(s)

John C Nash and Duncan Murdoch

References

Nash, J. C. (1979, 1990) _Compact Numerical Methods for Computers. Linear Algebra and Function Minimisation._ Adam Hilger./Institute of Physics Publications

Nash, J. C. (2014) _Nonlinear Parameter Optimization Using R Tools._ Wiley

nlsr documentation built on Sept. 8, 2023, 5:48 p.m.