MatFit: Wrapper Function to Estimate a 2- or 3-Parameters Logistic...

In LifeHist: Life History Models of Individuals

Description

Define the logistic model, pass the initial parameter values, the data, and the numerical optimization method(s) to estimate the model and to organize results in a list.

Usage

MatFit(p, par, matdat, method, control = ls(), itnmax)

Arguments

p	Integer, either 2 or 3, determining if the asymptotic proportion is assumed known at 1 or estimated.
par	Numeric vector of 2 or 3 components with initial paramater values.
matdat	A data.frame where each row is an individual fish, and the columnds are a continuous predictor, and ordinal keys for sex, month, and maturity stage.
method	Character or character vector, defining the list of numerical methods to use. See help pages for function optimx().
control	List, optimization control parameters to be passed to optimx. See help for function optimx().
itnmax	Integer, maximum number of iterations, to be passed to optimx().

Details

The function controls the estimation process. From a valid value of p, it will define the proper process model, either 2-parameters or 3-parameters logistic regression. The 3-parameter model might be useful outside the reproductive season when not all adult individuals are ready to reproduce, whereas the 2-parameter model assumes that above certain value of the continuous predictor all individuals are reproducing.

The function will re-organize optimx()'s output by adding some items (AIC, standard errors, correlation matrix) and ignoring other items. The output is a list of results in a list of optimization methods.

Value

model	Type of model, matlik.2p or matlik.3p
method	Name of numerical optimization method
converg	Convergence message
kkt	The Karush-Kuhn-Tucker optimality conditions
AIC	The Akaike Information Criterion
pars	Maximum likelihood estimates of model parameters
num.grads	Numerical gradients at the maximum likelihood estimates
stdev	Estimated standard deviations of maximum likelihood estimates of model parameters
Cor	The estimated correlation matrix of maximum likelihood estimates of model parameters

Author(s)

Ruben H. Roa-Ureta

Examples

data(BlackAngler.lenmatdat)
BA.matlen     <- MatCount(matdat=BlackAngler.lenmatdat,
                          fem.key=2,
                          mal.key=1,
                          stage.key=2,
                          season.key=c(5,7))
plot(BA.matlen[[2]],
     pred.50=35,
     pred.95=55,
     pred.unit="Length (cm)",
     top.text="Males Rep Season",
     lwd=2, xlab="", ylab="")
par(mfrow=c(2,2), mar=c(4,4,1,1), oma=c(4,4,1,1))
plot(BA.matlen[[1]],pred.50=35,pred.95=55,pred.unit="Length (cm)",
     top.text="Males NoRep Season", lwd=2, xlab="", ylab="")
plot(BA.matlen[[2]],pred.50=35,pred.95=55,pred.unit="Length (cm)",
     top.text="Males Rep Season", lwd=2, xlab="", ylab="")
plot(BA.matlen[[3]],pred.50=85,pred.95=110,pred.unit="Length (cm)",
     top.text="Females NoRep Season", lwd=2, xlab="", ylab="")
plot(BA.matlen[[4]],pred.50=55,pred.95=75,pred.unit="Length (cm)",
     top.text="Females Rep Season", lwd=2, xlab="", ylab="")
require(optimx)
BA.matlen.mal <- MatFit(p=2,
                        par=c(35,55),
                        matdat=BA.matlen[[2]],
                        method=c("spg", "CG", "Nelder-Mead"),
                        itnmax=100)
#
plot(BA.matlen[[2]],
     pred.50=BA.matlen.mal[[1]]$par.mle[1],
     pred.95=BA.matlen.mal[[1]]$par.mle[2],
     pred.unit="Length (cm)",
     top.text="Males Reproductive Season",
     lwd=2,
     xlab="Length (cm)",
     ylab="Proportion Mature")

LifeHist documentation built on May 2, 2019, 5:42 a.m.