grotag: Maximum likelihood estimation of growth and growth...

In fishmethods: Fishery Science Methods and Models

Maximum likelihood estimation of growth and growth variability from tagging data - Francis (1988)

Description

This function estimates parameters of Francis (1988)'s growth model using tagging data. The data are fitted using a constrained maximum likelihood optimization performed by optim using the "L-BFGS-B" method.

Usage

grotag(L1 = NULL, L2 = NULL, T1 = NULL, T2 = NULL, alpha = NULL, beta = NULL, 
 design = list(nu = 0, m = 0, p = 0, sea = 0), 
 stvalue = list(sigma = 0.9, nu = 0.4, m = -1, p = 0.1, u = 0.4, w = 0.4), 
 upper = list(sigma = 5, nu = 1, m = 2, p = 1, u = 1, w = 1), 
 lower = list(sigma = 0, nu = 0, m = -2, p = 0, u = 0, w = 0), gestimate = TRUE, 
 st.ga = NULL, st.gb = NULL, st.galow = NULL, st.gaup = NULL, st.gblow = NULL,
 st.gbup = NULL, control = list(maxit = 10000))

Arguments

L1	Vector of length at release of tagged fish
L2	Vector of length at recovery of tagged fish
T1	Vector of julian time at release of tagged fish
T2	Vector of julian time at recovery of tagged fish
alpha	Numeric value giving an arbitrary length alpha
beta	Numeric value giving an arbitrary length beta (beta > alpha)
design	List specifying the design of the model to estimate. Use 1 to designate whether a parameter(s) should be estimated. Type of parameters are: nu=growth variability (1 parameter), m=bias parameter of measurement error (1 parameter), p=outlier probability (1 parameter), and sea=seasonal variation (2 parameters: u and w). Model 1 of Francis is the default settings of 0 for nu, m, p and sea.
stvalue	Starting values of sigma (s) and depending on the design argument, nu, m, p, u, and w used as input in the nonlinear estimation (function optim) routine.
upper	Upper limit of the model parameters' (nu, m, p, u, and w) region to be investigated.
lower	Lower limit of the model parameters' (nu, m, p, u, and w) region to be investigated.
gestimate	Logical specifying whether starting values of ga and gb (growth increments of alpha and beta) should be estimated automatically. Default = TRUE.
st.ga	If gestimate=FALSE, user-specified starting value for ga.
st.gb	If gestimate=FALSE, user-specified starting value for gb.
st.galow	If gestimate=FALSE, user-specified lower limit for st.ga used in optimization.
st.gaup	If gestimate=FALSE, user-specified upper limit for st.ga used in optimization.
st.gblow	If gestimate=FALSE, user-specified lower limit for st.gb used in optimization.
st.gbup	If gestimate=FALSE, user-specified upper limit for st.gb used in optimization.
control	Additional controls passed to the optimization function optim.

Details

The methods of Francis (1988) are used on tagging data to the estimate of growth and growth variability. The estimation of all models discussed is allowed. The growth variability defined by equation 5 in the reference is used throughout.

Value

table	list element containing the model output similar to Table 3 of Francis (1988). The Akaike's Information Criterion (AIC) is also added to the output.
VBparms	list element containing the conventional paramaters of the von Bertalanffy model (Linf and K).
correlation	list element containing the parameter correlation matrix.
predicted	list element containing the predicted values from the model.
residuals	list element containing the residuals of the model fit.

Author(s)

Marco Kienzle [e-mail]

Gary A. Nelson, Massachusetts Division of Marine Fisheries [e-mail]

References

Francis, R.I.C.C., 1988. Maximum likelihood estimation of growth and growth variability from tagging data. New Zealand Journal of Marine and Freshwater Research, 22, p.42-51.

See Also

grotagplus

Examples

data(bonito)

#Model 4 of Francis (1988)
with(bonito,
 grotag(L1=L1, L2=L2, T1=T1, T2=T2,alpha=35,beta=55,
 	design=list(nu=1,m=1,p=1,sea=1),
 	stvalue=list(sigma=0.9,nu=0.4,m=-1,p=0.2,u=0.4,w=0.4),
 	upper=list(sigma=5,nu=1,m=2,p=0.5,u=1,w=1),
 	lower=list(sigma=0,nu=0,m=-2,p=0.0,u=0,w=0),control=list(maxit=1e4)))

fishmethods documentation built on April 27, 2023, 9:10 a.m.