optim.funfits {funfits} | R Documentation |
The object from a fitting procedure must have a predict function. Optim calls the function nlminb which finds a local minimum of a smooth nonlinear function subject to bounded-constrained paramenters.
optim.funfits(fit, start, maximize=T, lower, upper, ...)
fit |
Object from a fitting procedure. A predict function for the fit must be available. |
start |
Starting value for the search, the default is the middle of the region. |
maximize |
Default is to search for the maximum. |
lower |
Lower bound of parameters for optimization, default is the minimum of the data. |
upper |
Upper bound of parameters for optimization, default is the maximum of the data. |
... |
Optional arguments. |
parameters |
Final value of parameters at over which the optimization takes place. |
objective |
Final value of the objective function. |
message |
Statement of the reason for termination. |
grad.norm |
Final norm of the objective gradient. |
iterations |
Total number of iterations before terminiation. |
f.evals |
Total number of residual evaluations before the termination. |
g.evals |
Total number of jacobian evaluations before the termination. |
hessian |
Final value of the Hessian matrix, only if hessian is supplied intially. |
scale |
Final value of scale vector. |
nlminb, predict.tps, predict.krig, predict.nnreg
tps(BD[,1:4],BD$lnya,scale.type="range") -> fit # fitting a DNA strand # displacement amplification surface to various buffer compositions surface(fit) # plots fitted surface and contours optim(fit) # find surface optimum