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)
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.
A function to compute the residuals of our model at a parameter 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.
modss calls resfn to compute residuals and then uses
to compute the sum of squares.
At 2012-4-26 there is no checking for errors.
Note that it appears awkward to use this function in calls to optimization routines. The author would like to learn why.
The numeric value of the sum of squares at the paramters.
Special notes, if any, will appear here.
John C Nash <email@example.com>
Nash, J. C. (1979, 1990) _Compact Numerical Methods for Computers. Linear Algebra and Function Minimisation._ Adam Hilger./Institute of Physics Publications
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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) myssval <- modss(p, myres, tt = tt, y = y) cat("ss at (1,1,1) (should be 23520.58) = ",myssval,"\n")
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