FlexParamCurve-package {FlexParamCurve}

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. Note that this step is

unnecessary when using the model selection routines pn.mod.compare and pn.modselect.step as they

will automatically call modpar if parameter estimates are missing.

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 (specified pn.options object).

Note if required model is monotonic (i.e. contains no recession parameters, modno= 12 or 32) recessional parameters

will be ignored unless "force.nonmonotonic" option is TRUE in the specified pn.options list

object (see modpar) in which case they will be included as fixed values from the list object.

Parameter bounds can be refinedto improve fits by altering this list, either manually or using

change.pnparameters.

4) Plot your curves using 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

        

Note

Version 1.5 saves many variables, and internal variables in the package environment:

FlexParamCurve:::FPCEnv. By default, the pn.options file is copied to the environment

specified by the functions (global environment by default). Model selection routines

also copy from FPCenv to the global environment all nlsList models fitted during

model selection to provide backcompatibility with code for earlier versions. The user

can redirect the directory to copy to by altering the Envir argument when calling the

function.

Author(s)

Stephen Oswald <[e-mail]>

References

## Oswald, S.A. et al. 2012. FlexParamCurve: R package for flexible

     fitting of nonlinear parametric curves. Methods in Ecology and Evolution. 3(6): 1073-77.

     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 (and comments) at:

     http://www.methodsinecologyandevolution.org/view/0/VideoPodcastArchive.html#allcontent)

                      

See Also

nlme

SSlogis

Examples

#Code is provided here for an illustrative overview of using FlexParamCurve to select,  

# fit, analyze and plot the most appropriate non-linear curves for a dataset.

# NOTE: autorun is disabled for these examples since more detailed examples are provided for the 

# individual functions in their associated help files and runtime for this overview approximates 

# 5 mins. To run, simply copy and paste code from this help file into the R GUI.



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

## Not run:   

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

    posneg.data$id, existing = FALSE, pn.options = "myoptions")
## End(Not run)



# run backwards stepwise model selection (Step 2) for logist.data object

#without need to run any previous functions

## Not run:   

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

    logist.data$id, existing = FALSE, pn.options = "myoptions")
## End(Not run)



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

## Not run:   

     modpar(posneg.data$age,posneg.data$mass, pn.options = "myoptions")
## End(Not run)



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

## Not run:   

      change.pnparameters(M=1,RM=0.5,first.y=45.5, pn.options = "myoptions")
## End(Not run)

    

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

# fixed to value in pnmodelparams and then fit nlme model

## Not run:   

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))
## End(Not run)

 

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

## Not run:   

 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)
## End(Not run)

 

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

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

#create list for fixed parameters

## Not run:   

modpar(logist.data$age,logist.data$mass,force4par=TRUE, pn.options = "myoptions")

change.pnparameters(M=1, pn.options = "myoptions") # 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 )
## End(Not run)



#(Step 4)

## Not run:   

curve(posnegRichards.eqn(x, Asym = par[1], K = par[2], Infl = par[3], modno = 20

, pn.options = "myoptions"), add= TRUE)
## End(Not run)