rms.curv
Relative Curvature Measures for Non-Linear Regression
Description
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
Usage
rms.curv(obj)
Arguments
obj | Fitted model object of class |
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^θ and c^ι (unmultiplied), and C
the C-array 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
# 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
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Licensed under the GNU General Public License.