FlexParamCurve (version 1.3)

modpar: Estimate Values to be Used for Fixed FlexParamCurve Parameters

Description

This function creates the object pnmodelparams which holds estimates of values for all 8 FlexParamCurve parameters used for fitting and solving positive-negative Richards curves with SSposnegRichards and posnegRichards_eqn, respectively.

Usage

modpar(x,

y,

first_y = NA,

x_at_first_y = NA,

last_y = NA,

x_at_last_y = NA,

twocomponent_age = NA,

verbose = FALSE,

force8par = FALSE,

force4par = FALSE)

Arguments

x

a numeric vector of primary predictor variable

y

a numeric vector of response variable

first_y

the value of y at minimum x when it is required to be constrained

x_at_first_y

the final value of x - 0 value is used if not specified when last_y is not NA

last_y

the value of y at maximum x when it is required to be constrained

x_at_last_y

the final value of x - must be specified if last_y is not NA

twocomponent_age

a numerical specifying the age of intersection if a double model of two separate components is to be fitted. Alternatively a logical of value = TRUE if the same type of model is to be fitted but the age of intersection is unknown

verbose

logical indicating whether information on successful optimization and parameters should be returned during when using SSposnegRichards

force8par

logical specifying whether parameters of the negative Richards curve should be set to defaults if they cannot be estimated

force4par

logical specifying whether parameters of the negative Richards should be ignored - effectively using simple Richards curve

Value

• a list of estimated fixed values for all above arguments

Details

This function creates the object pnmodelparams which holds estimates of values for all 8 FlexParamCurve parameters used for fitting and solving positive-negative Richards curves with SSposnegRichards and posnegRichards_eqn, respectively. Running this function concurrently creates the object pnmodelparamsbounds which holds the maximum and minimum values for parameters to be used by optim and nls during parameter estimation. For definitions of parameters see either SSposnegRichards or posnegRichards_eqn. These objects are updated globally and do not need to be assigned by the user: the output of a return value simply offers a way to save values elsewhere for posterity. Estimates are produced by fitting positive-negative or double Richards curves in nls using SSposnegRichards for the full 8 parameter model (R1). If this fails, the function getInitial is called to attempt to produce initial estimates using the same 8 parameter model. If this also fails, estimates are attempted in the same way using the 4 parameter (positive only) model (R12). In this case, only the positive parameters are returned (NAs are substituted for negative parameters) unless argument force8par=TRUE, in which case negative parameters are defaulted to: RAsym = 0.05*Asym, Rk = K, Ri = Infl, RM = M. This function can now fit bilogistic (and more generally biRichards) curves, where the final curve is effectively either two positive curves or two negative curves, as well as negative-positive curves. This functionality is default and does not need to be specified. Parameter bounds estimated here for use in optim and nls fits within SSposnegRichards are applicable to a wide range of curves, although user may change these manually in list object pnmodelparamsbounds Bounds are estimated by modpar by adding or subtracting multiples of fixed parameter values to estimated mean parameter values: -Asym*0.5 and +Asym*2.5, -K*0.5 and +K*0.5, -Infl*2.5 and +Infl*10 -M*2 and +M*2 -RAsym*0.5 and +RAsym*2.5, -Rk*0.5 and +Rk*0.5, -Ri*2.5 and +Ri*5 -RM*2 and +RM*2. Use force8par = TRUE if initial call to modpar produces estimates for only 4 parameters and yet an 8 parameter model is desired for SSposnegRichards or posnegRichards_eqn. Use force4par = TRUE if you desire to produces estimates only for the four parameters of a single Richards curves. This should also be used if you wish to fit simple logistic Gompertz or von Bertalanffy curves: see SSposnegRichards for more details. When specified, first_y and last_y are saved in pnmodelparams to instruct SSposnegRichards to add this as the first or last value of the response, respectively, during estimation. To fit two-component double-curves, in which one curve equation is used up to (and including) the age of intersection and a separate equation is used for ages greater than the age of intersection the argument twocomponent_age should be set to the value for the age of intersection. If this argument is anything other than NA then a two-component model will be fitted when SSposnegRichards is called. This option will be saved in pnmodelparams and can be changed at will. If the argument verbose = TRUE then details concerning the optimization processes within SSposnegRichards are printed on screen whenever SSposnegRichards is called. These include whether optimization of the positive, negative parts of the curve or simultaneous optimizations are successful, if these have been further refined by nls, whether default parameters were used or the parameterization was aborted and what parameter values were finally exported by SSposnegRichards. This option will be saved in pnmodelparams and can be changed at will.

Examples

# estimate fixed parameters use data object posneg_data

   	 data(posneg_data)

   	 modpar(posneg_data$age, posneg_data$mass)



# estimate fixed parameters use data object posneg_data (only first 

# 4 group levels for example's sake) and specify a fixed hatching 

# mass for curve optimization using \code{\link{SSposnegRichards}}

modpar(posneg_data$age, posneg_data$mass)

   	 subdata <- subset(posneg_data,posneg_data$id == as.character(36)

	    		| posneg_data$id == as.character(9) 

	    		| posneg_data$id == as.character(32) 

	    		| posneg_data$id == as.character(43))

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

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

        	 modno = 22), data = subdata)

   		 

# fit a two component model - enter your own data in place of "mydata"

\dontrun{

 	modpar<-(mydata$x,mydata$y,twocomponent_age=TRUE) # if age of intersection unknown

 	modpar<-(mydata$x,mydata$y,twocomponent_age=75) # if age of intersection = 75

 	richardsR1.nls <- nls(y~ SSposnegRichards(x, Asym = Asym, K = K,

  		 Infl = Infl, M = M, RAsym = RAsym, Rk = Rk, Ri = Ri, RM = RM, modno = 1)

                        , data = mydata)

}



# force an 8 parameter estimate on logistic data

data(logist_data)

modpar<-(logist_data$age,logist_data$mass,force8par=TRUE)



# force an 4 parameter model on logistic data

data(logist_data)

modpar<-(logist_data$age,logist_data$mass,force4par=TRUE)



# troubleshoot the fit of a model 

data(posneg_data)

modpar<-(posneg_data$age,posneg_data$mass,verbose=TRUE)