Description Usage Arguments Details Value Note Author(s) References See Also Examples
Given a nonlinear model expressed as an expression of the form lhs ~ formula_for_rhs and a start vector where parameters used in the model formula are named, attempts to find the minimum of the residual sum of squares using the Nash variant (Nash, 1979) of the Marquardt algorithm, where the linear subproblem is solved by a qr method.
1 2 
formula 
This is a modeling formula of the form (as in 
start 
A named parameter vector. For our example, we could use start=c(b1=1, b2=2.345, b3=0.123) 
trace 
Logical TRUE if we want intermediate progress to be reported. Default is FALSE. 
data 
A data frame containing the data of the variables in the formula. This data may, however, be supplied directly in the parent frame. 
lower 
Lower bounds on the parameters. If a single number, this will be applied to all parameters. Default Inf. 
upper 
Upper bounds on the parameters. If a single number, this will be applied to all parameters. Default Inf. 
masked 
Character vector of quoted parameter names. These parameters will NOT be altered by the algorithm. 
control 
A list of controls for the algorithm. These are:

... 
Any data needed for computation of the residual vector from the expression rhsexpression  lhsvar. Note that this is the negative of the usual residual, but the sum of squares is the same. 
nlxb
attempts to solve the nonlinear sum of squares problem by using
a variant of Marquardt's approach to stabilizing the GaussNewton method using
the LevenbergMarquardt adjustment. This is explained in Nash (1979 or 1990) in
the sections that discuss Algorithm 23. (?? do we want a vignette. Yes, because
folk don't have access to book easily, but finding time.)
In this code, we solve the (adjusted) Marquardt equations by use of the
qr.solve()
. Rather than forming the J'J + lambda*D matrix, we augment
the J matrix with extra rows and the y vector with null elements.
A list of the following items
coefficients 
A named vector giving the parameter values at the supposed solution. 
ssquares 
The sum of squared residuals at this set of parameters. 
resid 
The residual vector at the returned parameters. 
jacobian 
The jacobian matrix (partial derivatives of residuals w.r.t. the parameters) at the returned parameters. 
feval 
The number of residual evaluations (sum of squares computations) used. 
jeval 
The number of Jacobian evaluations used. 
Special notes, if any, will appear here.
John C Nash <[email protected]>
Nash, J. C. (1979, 1990) _Compact Numerical Methods for Computers. Linear Algebra and Function Minimisation._ Adam Hilger./Institute of Physics Publications
others!!
Function nls()
, packages optim
and optimx
.
1  cat("See examples in nlmrtpackage.Rd\n")

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