rms.curv(obj, fit.val=<<see below>>, data=obj$call$data)
nls. The model must be fitted using the
default algorithm.
deriv3 of David Smith. Extracted from the
fitted model object by default.
rms.curv with components
pc and
ic for parameter
effects and intrinsic relative curvatures multiplied by sqrt(F),
ct and
ci
for c^theta and c^iota (unmultiplied), and
C the C-array as used in
section 7.3.1 of Bates & Watts.
The method of section 7.3.1 of Bates & Watts is implemented. The
function
deriv3 should be used generate a model function with first
derivative (gradient) matrix and second derivative (Hessian) array
attributes. This function should then be used to fit the nonlinear
regression model.
A print method,
print.rms.curv, prints the
pc and
ic components
only, suitably annotated.
If either
pc or
ic exceeds some threshold (0.3 has been suggested) the
curvature is unacceptably high for the planar assumption.
Bates, D. M, and Watts, D. G. (1988) Nonlinear Regression Analysis and its Applications. Wiley, New York.
# The treated sample from the Puromycin data
mmcurve <- deriv3(~ Vm * conc/(K + conc), c("Vm", "K"),
function(Vm, K, conc) NULL)
Treated <- Puromycin[Puromycin$state == "treated", ]
Purfit1 <- nls(vel ~ mmcurve(Vm, K, conc), data=Treated,
start=list(Vm=200, K=0.1))
rms.curv(Purfit1)