# Exploratory Growth Modeling with Age and Length Data For a data object of class 'GroAgeData' examine the goodness of initial guessed values of model parameters by making predictions and calculating residuals. ```r groageexp(x, m, unsex.action = NULL, par, distr) ``` ## Arguments - `x`: An object of class 'GroAgeData'. - `m`: Character, the specific growth model to be examined, either 'vonBer' (von Bertalanffy), 'schnute1', 'schnute2', or 'schnute3' - `unsex.action`: Character, optionally, when there are unsexed individual, what to do with these, either 'split' (random assignment of half to males and half to females), 'males' (complete assignment to males), 'females' (complete assignment to females), or 'ignore'. - `par`: Numeric, the logarithm of the vector of initial parameter values. - `distr`: Character, the distribution of the data, either 'apnormal' (adjusted profile approximation to the normal likelihood), 'aplnormal' adjusted profile approximation to the lognormal likelihood), 'normal', 'lognormal', or 'gamma'. ## Details When the distribution of the data is 'normal', 'lognormal', or 'gamma', an additional dispersion parameter has to be provided along with the growth model parameters, and placed at the end of the parameter vector. ## Returns A list with class attribute 'GroAgeExp' with the following components: - **Properties**: Properties of the data, passed from 'x' - **Data**: A data.frame with age, observed length, predicted length, and 'deviance' residuals - **Data**: Optionally, the same as previous component but for the other sex ## References Arkhipkin, A., Roa-Ureta, R. 2005. Identification of growth models for squids. Marine and Freshwater Research 56:371-386. Roa-Ureta, R. 2010. A likelihood-based model of fish growth with multiple length frequency data. Journal of Agricultural, Biological and Environmental Statistics 15:416-429 ## Author(s) Ruben H. Roa-Ureta ## Note Model types 'schnute1', 'schnute2', 'schnute3' have 5, 3, and 2 parameters respectively. 'schnute1' is described in Arkhipkin and Roa-Ureta (2005) whereas 'schnute2' and 'schnute3' are described in Roa-Ureta (2010). ## Examples ```r #Schnute - Normal data(ksbream) KSBream.AgeLen <- as.GroAgeData(x=ksbream, sex="Total", maleskey=1, femaleskey=2, coldate=1, colsex=9, colage=11, collen=2, colbw=5, colliver=7, colgonad=8, lentype="Total", unitsage="Years", unitslen="mm", unitsbw="g", unitsliver="g", unitsgonad="g", spec="KSBream") #par ini l1.f <- 175 a1.f <- 1 mu.f <- 250 g1.f <- 0.15 g2.f <- 0.1 l1.m <- 175 a1.m <- 1 mu.m <- 210 g1.m <- 0.25 g2.m <- 0.1 psi.f <- 0.5 psi.m <- 0.45 par.ini <- log(c(l1.f,a1.f,mu.f,g1.f,g2.f,l1.m,a1.m,mu.m,g1.m,g2.m,psi.f,psi.m)) KSBream.AgeLen.exp <- groageexp(x=KSBream.AgeLen, unsex.action="split", m="schnute1", par=par.ini, distr="normal") plot(KSBream.AgeLen.exp) ```

groageexp_1 function