rms.curv
Calculates the root mean square parameter effects and intrinsic relative curvatures, \(c^\theta\) and \(c^\iota\), for a fitted nonlinear regression, as defined in Bates & Watts, section 7.3, p. 253ff
 Keywords
 nonlinear
Usage
rms.curv(obj)
Arguments
 obj

Fitted model object of class
"nls"
. The model must be fitted using the default algorithm.
Details
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.
Value
A list of class 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 Carray as used in section 7.3.1 of Bates & Watts.
References
Bates, D. M, and Watts, D. G. (1988) Nonlinear Regression Analysis and its Applications. Wiley, New York.
See Also
Examples
library(MASS)
# 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(rate ~ 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