MQMF (version 0.1.1)

spm: spm calculates the dynamics of a Schaefer or Fox model

Description

spm calculates the dynamics using a Schaefer of Fox model. The outputs include predicted Biomass, year, catch, cpue, predicted cpue, contributions to q, ssq, and depletion levels. Generally it would be more sensible to use simpspm when fitting a Schaefer model or a Fox model as those functions are designed to generate only the log of predicted cpue as required by the functions ssq and negLL, but the example shows how it could be used. The function spm is used inside 'plotspmmod' and could be used alone, to generate a full list of model outputs after the model has been fitted. spm is designed for working with a single vector of an index of relative abundance. If there are multiple vectors of the index then use simpspmM and spmCE.

Usage

spm(inp, indat, schaefer = TRUE, year = "year", cats = "catch", index = "cpue")

Arguments

inp

a vector of 3 or 4 model parameters (r,K,sigma) or (r, K, Binit,sigma), you would use the latter if it was suspected that the fishery data started after some initial depletion had occurred. The sigma is an estimate of the variation of the cpue through time. This is required but is only used when fitting the model using negative log-likelihoods.

indat

a matrix with at least columns year, catch, and cpue

schaefer

a logical value determining whether the spm is to be a simple Schaefer model (p=1) or approximately a Fox model (p=1e-08). The default is TRUE

year

the column name of the year variable (in case your dataset names it fishingyearinwhichthecatchwastaken), default='year'

cats

column name of the catch variable, default='catch'

index

the name of the cpue variable, default='cpue'

Value

a list of five objects; parameters plus q, then outmat, the matrix with the dynamics, msy the maximum sustainable yield, and sumout, which contains r,K,B0,msy,p,q,Depl, FinalB, and InitDepl

Examples

# NOT RUN {
 data(abdat)   # spm is used inside plotspmmod
 pars <- log(c(0.35,7800,3500,0.05))
 ans <- plotspmmod(pars,abdat) #not fitted, just guessed
 bestSP <- fitSPM(pars=pars,fish=abdat,funk=simpspm)
 outfit(bestSP)  # best fitting estimates
 ans <- plotspmmod(bestSP$estimate,abdat,schaefer=TRUE)
 str(ans)
# }