FlexParamCurve-package {FlexParamCurve} | R Documentation |
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 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 . 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 and
as they
will automatically call if parameter estimates are missing.
2) Determine most appropriate model (number of necessary parameters) for your data
using or
(these rank competing model and then compare nested models using
). This may take some time as many
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 or
or
models using
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 ) 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
4) Plot your curves using specifying the appropriate model number and list of parameters/options.
User level functions include:
all-model selection for positive-negative Richards nlsList models
backward stepwise model selection for positive-negative Richards nlsList models
selfStart function for estimating parameters of 36 possible reductions of the 8-parameter positive-negative Richards model (double-Richards)
function for evaluating 36 possible reductions of the 8-parameter positive-negative Richards model (double-Richards)
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)
simple function to update pnmodelparams and pnmodelparamsbounds with user specified values
performs extra sum-of-squares F test for two nested nlsList models
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 <steve.oswald@psu.edu>
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
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)