For a nonlinear model originally expressed as an expression of the form lhs ~ formula_for_rhs assume we have a resfn and jacfn that compute the residuals and the Jacobian at a set of parameters. This routine computes the gradient, that is, t(Jacobian) . residuals.

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`prm` |
A parameter vector. For our example, we could use start=c(b1=1, b2=2.345, b3=0.123) However, the names are NOT used, only positions in the vector. |

`resfn` |
A function to compute the residuals of our model at a parameter vector. |

`jacfn` |
A function to compute the Jacobian of the residuals at a paramter vector. |

`...` |
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. |

`modgr`

calls resfn to compute residuals and jacfn to compute the
Jacobian at the parameters `prm`

using external data in the dot arguments.
It then computes the gradient using t(Jacobian) . residuals.

Note that it appears awkward to use this function in calls to optimization routines. The author would like to learn why.

The numeric vector with the gradient of the sum of squares at the paramters.

Special notes, if any, will appear here.

John C Nash <nashjc@uottawa.ca>

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

Function `nls()`

, packages `optim`

and `optimx`

.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
cat("See examples in nlmrt-package.Rd\n")
y <- c(5.308, 7.24, 9.638, 12.866, 17.069, 23.192, 31.443, 38.558,
50.156, 62.948, 75.995, 91.972) # for testing
tt <- seq_along(y) # for testing
f <- y ~ b1/(1 + b2 * exp(-1 * b3 * tt))
p <- c(b1 = 1, b2 = 1, b3 = 1)
myres <- model2resfun(f, p)
myjac <- model2jacfun(f, p)
mygr <- model2grfun(f, p)
gr <- mygr(p, tt = tt, y = y)
grm <- modgr(p, myres, myjac, tt = tt, y = y)
cat("max(abs(grm - gr)) =",max(abs(grm-gr)),"\n")
``` |

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