simpspmM {MQMF}

simpspmM calculates predicted CE when multiple indices are present

Description

simpspmM calculates the predicted CPUE for an surplus production model and is designed for when there are more than one time-series of an index of relative abundance. The parameter vector includes the standard deviations for the log-normal distributions assumed for the indices of relative abundance. They are not used in this function but will be when the likelihoods are calculated as a next step in fitting the model.

Usage

simpspmM(
  pars,
  indat,
  schaefer = TRUE,
  year = "year",
  cats = "catch",
  index = "cpue"
)

Arguments

pars

the parameters of the SPM = r, K, Binit if fishery is depleted to start with (omit otherwise), and a sigma for each cpue series. Each parameter is in log space and is back-transformed inside simpspmM.

indat

the data which needs to include year, catch, and cpue. The latter should have a separate column for each fleet, with a column name beginning with cpue or whatever name you put in index (see below) for example cpue1, cpue2, etc.

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

column name within indat containing the years, default='year'

cats

column name within indat containing the catches, default='catch'

index

the prefix in the column names given to the indices of relative abundance used, perhaps 'cpue' as in cpueTW, cpueAL, etc. grep is used to search for columns containing this prefix to identify whether there are more than one column of cpue data. Be sure to only use the prefix for indices of relative abundance.

Value

a vector or matrix of nyrs of the predicted CPUE

Examples

 data(twoindex)
 fish <- as.matrix(twoindex)
 pars <- log(c(0.04,155000,0.4,0.3))
 bestSP <- nlm(f=negLLM,p=pars,funk=simpspmM,indat=fish,
             schaefer=TRUE,logobs=log(fish[,c("cpue1","cpue2")]),
             steptol=1e-06,harvpen=TRUE)
 outfit(bestSP)  # best fitting estimates
 simpspmM(bestSP$estimate,fish) # the two log(predicted cpue)