FlexParamCurve (version 1.4-3)

FlexParamCurve-package: Tools to Fit Flexible Parametric Curves

Description

selfStart functions and model selection tools to fit parametric curves in nls, nlsList and nlme frameworks.

Details

General approach for using package (also see examples below) 1) Run $modpar$ to produce initial parameter estimates and estimates of parameter bounds for your dataset. These are used to accomodate fixed parameters and are saved in user-specified list object All parameters and options in this list can be edited manually or using $change.pnparameters$. The list could be created manually given that the elements were labelled sufficiently. 2) Determine most appropriate model (number of necessary parameters) for your data using $pn.mod.compare$ or $pn.modselect.step$ (these rank competing model and then compare nested models using $extraF$). This may take some time as many $nlsList$ objects are fitted. Note that if you perform this step, then you do not need to perform step 1. If you are sure of your model (e.g. it is a simple logistic) Step 2 may be unnecessary. 3) Fit $nls$ or $nlsList$ or $nlme$ models using $SSposnegRichards$ specifying the appropriate model number and the list of parameters and options. Parameter bounds can be refined to improve fits by altering this list, either manually or using change.pnparameters. 4) Plot your curves using eqn{posnegRichards.eqn} specifying the appropriate model number and list of parameters/options. User level functions include: $pn.mod.compare$ all-model selection for positive-negative Richards nlsList models $pn.modselect.step$ backward stepwise model selection for positive-negative Richards nlsList models $SSposnegRichards$ selfStart function for estimating parameters of 36 possible reductions of the 8-parameter positive-negative Richards model (double-Richards) $posnegRichards.eqn$ function for evaluating 36 possible reductions of the 8-parameter positive-negative Richards model (double-Richards) $modpar$ estimates mean parameters (and parameter bounds) for 8-parameter positive-negative Richards models or 4-parameter Richards models and saves in objects pnmodelparams and pnmodelparamsbounds. (required prior to use of the above functions) $change.pnparameters$ simple function to update pnmodelparams and pnmodelparamsbounds with user specified values $extraF$ performs extra sum-of-squares F test for two nested nlsList models $extaF.nls$ performs extra sum-of-squares F test for two nested nls models ll{Package: FlexParamCurve Type: Package Title: Tools to Fit Flexible Parametric Curves Version: 1.4-3 Date: 2012-09-23 Author: Stephen Oswald Maintainer: Stephen Oswald License: GPL-2 Depends: nlme Enhances: nlme LazyLoad: yes }

References

## Oswald, S.A. et al. (Early view) FlexParamCurve: R package for flexible fitting of nonlinear parametric curves. Methods in Ecology and Evolution. doi: 10.1111/j.2041-210X.2012.00231.x (see also tutorial and introductory videos at: http://www.methodsinecologyandevolution.org/view/0/podcasts.html (posted September 2012 - if no longer at this link, check the archived videos at: http://www.methodsinecologyandevolution.org/view/0/VideoPodcastArchive.html#allcontent

See Also

nlme SSlogis

Examples

# run all-model selection for posneg.data object (Step 2) without need to run any previous functions

data(posneg.data)

modseltable <- pn.mod.compare(posneg.data$age, posneg.data$mass,

    posneg.data$id, existing = FALSE, pn.options = "myoptions")



# run backwards stepwise model selection (Step 2) for logist.data object without need to run any previous functions

data(logist.data)

modseltable <- pn.modselect.step(logist.data$age, logist.data$mass,

    logist.data$id, existing = FALSE, pn.options = "myoptions")



# estimate fixed parameters use data object posneg.data (Step 1)

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



# change fixed values of M and constrain hatching mass to 45.5 in a growth curve (Step 1)

change.pnparameters(M=1,RM=0.5,first.y=45.5, pn.options = "myoptions")

    

# fit nlsList object using 6 parameter model with values M and RM (Step 3)

# fixed to value in pnmodelparams and then fit nlme model

richardsR22.lis <- nlsList(mass ~ SSposnegRichards(age, Asym = Asym, K = K,

      Infl = Infl, RAsym = RAsym, Rk = Rk, Ri = Ri,

      modno = 22, pn.options = "myoptions"), data = posneg.data)

 richardsR22.nlme <- nlme(richardsR22.lis, random = pdDiag(Asym + Infl ~ 1))

 

# fit reduced nlsList model and then compare performance with extraF (manual version of Step 2)

richardsR20.lis <- nlsList(mass ~ SSposnegRichards(age, Asym = Asym, K = K,

      Infl = Infl, modno = 20, pn.options = "myoptions"), data = posneg.data)

 extraF(richardsR20.lis,richardsR22.lis)

 

 # fit and plot a logistic curve (M=1) to data, note that all parameters set to equal 1 here are ignored

 # note code here forces \eqn{modpar} to only estimate 4 curve parameters (simple Richards curve)

data(logist.data)

modpar(logist.data$age,logist.data$mass,force4par=TRUE, pn.options = "myoptions")#create list for fixed parameters

change.pnparameters(M=1) # set M to 1 for subsequent fit

richardsR20.nls <- nls(mass ~ SSposnegRichards(age, Asym = Asym, K = K,

      Infl = Infl, modno = 20, pn.options = "myoptions"), data = logist.data)

plot(logist.data$age , logist.data$mass, xlab = "age", ylab = "mass", pch = ".", cex = 0.7) 

par <- coef( richardsR20.nls )

#(Step 4)

curve(posnegRichards.eqn(x, Asym = par[1], K = par[2], Infl = par[3], modno = 20), add= TRUE, pn.options = "myoptions")