extraF.nls: Compare Two nls Models Using Extra Sum-of-Squares F-Tests
In FlexParamCurve: Tools to Fit Flexible Parametric Curves

Description Usage Arguments Details Value Author(s) References See Also Examples

Function to compare two nested nls models using extra

sum-of-squares F-Tests.

extraF.nls(submodel,


genmodel)

`submodel`	nls model with fewer curve parameters (reduced model)
`genmodel`	nls model with more curve parameters (general model)

Models must be entered in the correct order with the reduced model appearing

first in the call and the more general model appearing later. These must be nested models,

i.e. the general model must contain all of the curve parameters in the reduced model and more.

Entering models with the same number of parameters will produce NAs in the output, but

the function will produce seemingly adequate output with non-nested models. The user must

check that models are nested prior to use.

This function is not promoted for use in model selection as differences in curves of

different grouping levels in the dataset may be obscured when curves are fitted to the

entire dataset, as in nls.

Extra sum-of-squares is obtained from:

1	F = (SS1 - SS2)/(df1 - df2) / (SS2 / df2)

where SS = sum-of-squares and df = degrees of freedom, for the more reduced model (1) and the

more general model (2), respectively. To account for missing individuals for different fits

df are scaled in all models to the value they would be if all individuals fit successfully (note

that if all individuals had the same fit, this would not influence extra sum of squares).

If the F value is significant then the more general model provides a significant improvement

over the reduced model, but if the models are not significantly different then the reduced

parameter model is to be preferred.

A data.frame listing the names of the models compared, F,

numerator degrees of freedom,

demonimator degrees of freedom, P value and the residual sum of squares for both the general

and reduced models

Stephen Oswald <steve.oswald@psu.edu>

Ritz, C. and Streibigg, J. C. (2008) Nonlinear regression with R.

Springer-Verlag, New York.

extraF

nls

pn.modselect.step

pn.mod.compare

#fit and compare two nested nls models (7 vs 8 parameter models)


   #create list for fixed parameters


   modpar(posneg.data$age, posneg.data$mass, pn.options = "myoptions") 


   richardsR1.nls <- nls(mass ~ SSposnegRichards(age, Asym = Asym, K = K,


   Infl = Infl, M = M, RAsym = RAsym, Rk = Rk, Ri = Ri, RM = RM, modno = 1, pn.options = myoptions)


                        , data = posneg.data)


   richardsR2.nls <- nls(mass ~ SSposnegRichards(age, Asym = Asym, K = K,


   Infl = Infl, M = M, RAsym = RAsym, Rk = Rk, Ri = Ri, modno = 2, pn.options = myoptions)


                        , data = posneg.data)


   extraF.nls(richardsR2.nls, richardsR1.nls)