rms.curv.RdCalculates 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
rms.curv(obj)
| obj | Fitted model object of class |
|---|
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 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(rate ~ mmcurve(Vm, K, conc), data = Treated, start = list(Vm=200, K=0.1)))#> Nonlinear regression model #> model: rate ~ mmcurve(Vm, K, conc) #> data: Treated #> Vm K #> 212.68363 0.06412 #> residual sum-of-squares: 1195 #> #> Number of iterations to convergence: 6 #> Achieved convergence tolerance: 6.096e-06rms.curv(Purfit1)#> Parameter effects: c^theta x sqrt(F) = 0.2121 #> Intrinsic: c^iota x sqrt(F) = 0.092##Parameter effects: c^theta x sqrt(F) = 0.2121 ## Intrinsic: c^iota x sqrt(F) = 0.092