rms.curv(obj, fit.val=<<see below>>, data=obj$call$data)
obj
|
Fitted model object of class nls. The model must be fitted using the
default algorithm.
|
fit.val
|
An optional fitted values vector with the gradient matrix and Hessian array
as attributes, as produced by the model function obtained by
using the function deriv3 of David Smith. Extracted from the
fitted model object by default.
|
data
| Optional data frame for variables. Extracted from the fitted model object call (if any data frame is specified) by default. |
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.
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.deriv3
### Not usable in R
> # 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)
Parameter effects: c^theta x sqrt(F) = 0.2121
Intrinsic: c^iota x sqrt(F) = 0.092