Description Details Note Author(s) References See Also Examples
selfStart functions and model selection tools to fit parametric curves in
nls
, nlsList
and nlme
frameworks.
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 eqnposnegRichards.eqn specifying the appropriate model number and list of parameters/options.
User level functions include:
pn.mod.compare
1 2 3 4 |
pn.modselect.step
1 2 3 4 | backward stepwise model selection for positive-negative Richards nlsList models
|
SSposnegRichards
1 2 3 4 5 6 7 |
posnegRichards.eqn
1 2 3 4 5 6 7 |
modpar
1 2 3 4 5 6 7 8 9 10 |
change.pnparameters
1 2 3 4 5 6 7 8 9 10 |
extraF
1 2 3 4 |
extaF.nls
1 2 3 4 |
Package: | FlexParamCurve |
Title: | Tools to Fit Flexible Parametric Curves |
Version: | 1.5-2 |
Date: | 2015-03-31 |
Author: | Stephen Oswald [aut, cre] |
Maintainer: | Stephen Oswald <steve.oswald@psu.edu> |
License: | GPL-2 |
Depends: | nlme |
LazyLoad: | yes |
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.
Stephen Oswald <steve.oswald@psu.edu>
## Oswald, S.A. et al. 2012. FlexParamCurve: R package for flexible
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | 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 at:
http://www.methodsinecologyandevolution.org/view/0/VideoPodcastArchive.html#allcontent)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | #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)
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