Relative Curvature Measures for Non-Linear Regression
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
Fitted model object of class
The method of section 7.3.1 of Bates & Watts is implemented. The
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
A print method,
print.rms.curv, prints the
ic components only, suitably annotated.
ic exceeds some threshold (0.3 has been
suggested) the curvature is unacceptably high for the planar assumption.
A list of class
rms.curv with components
for parameter effects and intrinsic relative curvatures multiplied by
ci for c^θ and c^ι (unmultiplied),
C the C-array as used in section 7.3.1 of Bates & Watts.
Bates, D. M, and Watts, D. G. (1988) Nonlinear Regression Analysis and its Applications. Wiley, New York.
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# 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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